Urologic Cancer Molecular Biology

*Pavel Onofrei, Viorel Dragoș Radu, Alina-Alexandra Onofrei, Stoica Laura, Doinita Temelie-Olinici, Ana-Emanuela Botez, Vasile Bogdan Grecu and Elena Carmen Cotrutz*

#### **Abstract**

An adequate understanding of the molecular mechanisms of the most common urological cancers is necessary for a correct approach to diagnosis, precise treatment, but also for the follow-up of these patients. It is necessary to understand the molecular mechanisms underlying the carcinogenic processes, the molecular pathways involved in this process, and also to describe the biomarkers useful for diagnosis but also for predictability, treatment, and natural history. In addition, it would be useful to describe a list of useful molecules currently under investigation as possible biomarkers to improve the income of cancer patients.

**Keywords:** prostate cancer, urothelial cancer, kidney cancer, biomarkers, bioinformatics

#### **1. Introduction**

Over the past decades, the treatment of localized cancers was mostly focused on surgery and radiotherapy and advanced neoplasia was treated using nonspecific cytotoxic agents. Despite the increasing 5-year survival rate, there is also still a large number of nonresponsive patients, mostly due to the diversity of genetic profiles among the worldwide population, also the heterogeneity within the tumor itself [1–3].

Neoplasia develops under a various number of molecular and genetic malfunctions that regulate cell division, cell differentiation, and programmed cell death [4, 5]. Tumor suppressor genes and proteins encoded by these genes play a major role in cellular growth regulation, cell signaling, and DNA repair. Oncogenes are mutated forms of normal genes and are associated with cellular proliferation.

Molecular biology focuses on the study of physiological and pathological changes in the body. It helps to develop tools for early diagnosis of these changes and ways to reverse them. In recent years, considerable efforts have been made to elucidate the molecular mechanisms of malignant transformation that have the role of personalized medicine (especially oncology) in order to maximize the effectiveness of the therapeutic response but also to minimize side effects. In this sense, understanding the process of carcinogenesis helps to diagnose at an early stage, an accurate diagnosis but also of the different behavior of tumor subtypes, in order to establish the appropriate therapy [6–11].

BCG (Calmette-Guérin bacillus) immunological therapy in the treatment of bladder cancer is an excellent starting point for the usefulness of molecular studies on immunotherapy in genitourinary cancers. Being a nonspecific agent, there are many gaps regarding its mechanism of action but it paved the way for a different approach, that of inducing an immune response against cancer via cancer vaccines. Prostate and kidney cancer are also considered for this kind of treatment [11].

From a clinical point of view, the most obvious mechanism is the limitation of the specific antigen immune response by CD4 and CD8 (tumor-infiltrating lymphocytes) with significant importance in limiting the antitumor response thus preventing a significant proportion the clinical remission of tumors. Thus, a therapeutic line has been developed that targets an immune checkpoint blockade in order to bypass the mechanisms that limit the response, and which in the case of bladder tumors, in combination with conventional chemotherapy, or VEGF inhibition (vascular endothelial growth factor) in kidney cancer and last but not least, in prostate cancer—hormone therapy, increase the effectiveness of treatment [11].

In this chapter, we discuss the most significant urological cancers including prostate cancer, urothelial carcinoma, and renal highlighting their molecular mechanisms and the related studied biomarkers for precision diagnosis and therapeutic management.

#### **1.1 Bioinformatics in urologic cancers**

Cancer is one of the most complex diseases to understand. It is characterized by the rapid growth and spread of its cells, its resistance to conventional treatments, and its ability to invade and displace normal tissue. Malignant cells, regardless of type, usually share some common features—reprogrammed energy metabolism, sustained cell growth signals, evasion of growth suppressors, resistance to apoptosis, facilitation of replicative immortality, induction of angiogenesis, resistance to destruction by the immune system, and promotion of cell invasion and metastasis. These recognized characteristics have led to a deeper understanding of this disease. However, the reality is that our overall ability to cure cancer has not yet improved significantly, especially for adult cancers, which account for 99% of all cancers [12–14].

The major challenges facing clinical oncologists include not only the considerable heterogeneity and different genetic backgrounds even within the same type of cancer, but also the fact that effective drugs lose their efficacy due to the ability of cancer to evolve rapidly, especially with regard to the emergence of drug-resistant subpopulations [12–14].

One of the many reasons why our knowledge is so sparse is the lack of molecularlevel data, the full analysis, and interpretation of which can reveal the full complexity of developing cancer. Although large amounts of genomic, epigenomic, transcriptomic, metabolomic, and proteomic data have been obtained for a variety of cancers, few cancer studies are designed to fully exploit all the information that can be derived from the available omic data [12–14]. Integrative analyses of multiple data types may prove to be essential to gain a full and systems-level understanding of cancer's evolution dynamics, including the elucidation of its true drivers as well as key facilitators at different developmental stages of cancer. We anticipate that only when all of the key information hidden in omic data can be fully derived and utilized can we expect a meaningful breakthrough in our understanding of cancer [12–14].

The understanding of the human genome combined with technologies such as DNA and protein arrays or mass spectrometry has improved the simultaneous study

#### *Urologic Cancer Molecular Biology DOI: http://dx.doi.org/10.5772/intechopen.101381*

of numerous genes and proteins in single experiments and has rekindled interest in the search for novel biomarkers for cancers such as but not limited to, renal, urothelial, and prostate cancers [15–21]. Modern technology allows for parallel studies as compared to the serial analyses used in older methods. This allows the identification of distinct patterns for cancer diagnosis and classification, as well as for prediction of therapeutic response. In addition, these technologies enable the discovery of new individual tumor markers through the use of acceptable hypotheses and novel analytical methods [15–21]. Although new technologies and tactics often fail in the discovery of established cancer biomarkers and focus on identifying high-incidence compounds, they have the potential to revolutionize biomarker discovery. It is now critical to focus on thorough validation studies to discover the most effective techniques and biomarkers and bring them to the clinic as quickly as possible [15–21].

Bioinformatics and computational techniques have been well applied in the studies of various tumors (urologic, digestive gynecologic, etc.), and confirmed to be efficient and reliable in identifying novel tumor markers for cancer diagnosis and targeted treatments [22].

The very large pool of publicly available cancer-omic data, which includes transcriptomic, genomic, metabolomic, and epigenomic data, contains a considerable amount of information about the activities of individual biochemical pathways, their dynamics, and the complex relationships between them, as well as information about various microenvironmental factors. When the right questions are asked, powerful statistical analysis techniques can be very helpful in uncovering such information. Such targeted questions provide a framework for hypothesis-driven data analysis and evaluation that can be used to test the validity of the formulated hypothesis and to formulate new questions that may lead to the discovery of specific pathways or even possible causal relationships between the activities of different pathways. More effective analysis methods for different omic data formats are definitely needed to answer more difficult and in-depth questions about the data, such as deconvolution of gene expression data obtained from tissue samples with different cell types and inference of causal relationships. Effective data mining and information discovery require integrative analysis of various forms of omic and computational data [14, 15, 17–21].

#### **1.2 Prostate cancer (PCa)**

It is considered the second most prevalent cancer among male subjects. Around one in eight men will get diagnosed with the illness during their lifetime. In 2012, around 1.1 million men were diagnosed with prostate cancer globally. Around one in 40 of them will die due to this disease [23–25].

With the discovery and introduction of PSA-based screening tests, the incidence of prostate cancer has increased dramatically. However, given the advances in molecular biology, we realize that a purely PSA-based test does not provide sufficient accuracy. To find an answer, we need to consider other possible screening methods by elucidating the molecular basis of cancer development and more specific biomarkers [23–25].

The complexity of the diagnostic process in prostate cancer is reflected in the various interactions that occur during the course of the disease itself. The initial changes that lead to cancer are usually caused by chronic inflammation and dietary habits. They eventually lead to severe damage to the DNA of the prostate cells. Early genetic events that can promote disease progression include fusions or mutations of various genes and oncogenes (**Figure 1**), as well as malfunctions of molecular signaling pathways [23–25].

#### **Figure 1.**

*Alterations that occur in the malignancy of prostatic tissue. PIA - proliferative inflammatory atrophy, PIN - prostatic intraepithelial neoplasia.*

The primary androgen of the prostate is dihydrotestosterone (DHT), and exposure to this androgen is considered to be a precipitating factor in the development of primary prostatic neoplasia. The androgen receptor (AR) plays a central role in the development and progression of prostate cancer. Although the relationship between androgen exposure and cancer is not yet clear, exposure to very high or low concentrations of this substance may be protective against PCa (prostate cancer). The effects of androgen on long-term male survival are still unknown. The interaction of vitamin D with its receptor may influence the aggressiveness of the disease and its risk factors, but the mechanism underlying this event is not yet fully understood [23–25].

The prostate develops just caudal to the bladder neck by the proliferation of epithelial buds growing from the urogenital sinus epithelium. Epithelial budding is strictly androgen-dependent and represents the first identifiable events in prostate development. Budding of the prostate requires complicated epithelial-mesenchymal interactions [26, 27].

High testosterone levels in male embryos promote prostate development. Testosterone is converted to DHT by 5α-reductase, an interaction that activates AR. High testosterone levels during early development led to prostate growth regardless of genetic sex, suggesting a primary role for androgens in prostate induction [26, 27].

The upregulation of Sox9 (sex-determining region Y-box 9), a transcription factor induced by the FGF pathway, is the earliest event that appears to occur in the epithelium during prostate development. This mechanism is followed by the increased expression of Nkx3.1 (NK homeobox transcription family member), which influences the degree of branching in the mature mouse prostate, where it may also act as a tumor suppressor [26, 27].

The FGF family of secreted peptides promotes cell growth by binding to cell surface proteins and activating multiple signaling cascades demonstrated for prostate, mammary and salivary glands, or lung. Fgf-7 (keratinocyte growth factor) and Fgf-10 are considered specific for the prostate [28, 29]. Fgf-7 (keratinocyte growth factor) and Fgf-10 are considered specific for the prostate. FGFR2 is expressed in developing prostatic epithelial cells (PrECs) and through interaction with Frs-2α. Both molecules are secreted by the mesenchyme of the prostate. This mechanism led to the hypothesis *Urologic Cancer Molecular Biology DOI: http://dx.doi.org/10.5772/intechopen.101381*

that they act as andromedins since they are associated with androgen-independent growth factors. Due to the absence of Fgf-10, the mice also showed prostatic hypoplasia [28, 29].

Wnt signaling plays a crucial role in the development of various organs, including the prostate. Essentially, it regulates proliferation and differentiation through a series of Wnt ligands expressed during prostate bud formation. Canonical Wnt targets, such as Lef1 and Axin2, are upregulated in prostate bud epithelium [30–32].

Prostate cancer is one of the few malignancies for which there is a clinically meaningful serum biomarker. From its discovery in 1979 to its clinical application in the late 1980s to 1990s, PSA has become an invaluable tool for detecting, grading, and monitoring prostate cancer in men.

There is also considerable overlap in serum PSA levels between men with cancer and those with the noncancerous disease. The presence of prostatic hyperplasia or inflammation may also explain the elevated serum PSA levels [33, 34]. To this end, the use of PSA derivatives such as PSA density, PSA velocity, age-adjusted values, and more recently molecular derivatives can be used to improve clinical decisions compared to the isolated use of PSA.

Several molecular approaches have been pursued in the search for the optimal biomarker for prostate cancer. An overview of basic cellular processes begins with a DNA sequence (gene) that is transcribed into mRNA (transcript) and then translated into a protein that can then perform a specific cellular function. A major goal of biomarker development is to identify the differences in the molecular structure of prostate cancer cells compared to their benign counterparts and also to distinguish the more aggressive phenotypes from the others. The identification and quantification of these molecular differences in tissues and body fluids form the basis for the discovery of biomarkers for prostate cancer.

PSMA (glycoprotein prostate-specific membrane antigen), a folate hydrolase, has been studied as a potential biomarker for prostate cancer in tissue, serum, or urine. It is found in the membrane of all prostate epithelial cells. It is a type II transmembrane protein with an extracellular C-terminus that exists as a dimer and binds glutamate and glutamate-like structures [35, 36]. Nowadays, PSMA is mainly used in targeted imaging and theranostics [37, 38]. In particular, 68Gallium positron emission tomography of prostate-specific membrane antigen (68Ga-PSMA PET) is increasingly used as a diagnostic tool in biochemical recurrence after primary therapy [39].

Human kallikrein peptidase 2 (hK2) shares many important properties with PSA and has demonstrated its potential as another tumor marker for prostate cancer. Among many other similarities, hK2 and PSA share 80% amino acid homology, show similar specificity for prostate tissue, and are hormonally regulated by androgens. One of the major functions of hK2 is to activate the zymogen (proPSA) to active PSA by cleaving the amino acid presequence. Critical to its utility as a biomarker is that hK2 expression varies independently of tissue and serum PSA expression. In BPH, PSA expression is highly expressed compared to the minimal immunoreactivity of hK2, but hK2 is also overexpressed in PCa compared to PSA. Furthermore, tissue expression of hK2 appears to correlate with more aggressive pathological features, including Gleason grade [40, 41].

Circulating tumor cells (CTCs) have long been touted as potential prognostic biomarkers and indicators of treatment response. Subsequent CTC research in prostate cancer has employed a wide range of methods utilizing characteristics such as size, surface marker expression, and cellular plasticity that distinguish CTCs from circulating blood mononuclear cells [42, 43]. Typically, CTCs are defined as

CD45 and positive for an epithelial marker such as epithelial cell adhesion molecule (EpCAM) and/or cytokeratin. Although the development of CTCs as biomarkers for prostate cancer has been relatively slow, there has been considerable recent progress in the field and a growing number of clinical trials. Currently, there is only one FDAapproved method for identifying CTCs: CellSearch, which uses antibodies to EpCAM for CTC detection and then stains with antibodies to CD45 and cytokeratins 8, 18, and 19 (positive) to identify individual CTCs. Using this system, a CTC count of five or more cells per 7.5 mL of blood at any time during disease progression has been associated with poor prognosis in the prostate, breast, and colorectal cancers [42, 43].

The ease of collection of urine and the excretion of prostate cells have long made it a potential biomarker source for the early detection of prostate cancer [44]. However, only recently have urine biomarkers for prostate cancer come into clinical use. The first of these biomarkers, described in 1999, prostate cancer antigen 3 (PCA3), is not expressed outside the prostate. Studies show that PCA3 levels in prostate cancer are far higher than those in BPH, but the function of the antigen is still unknown [45, 46]. Recent studies used RT-PCR to detect PCA3 in urine and showed that PCA3 performs better than PSA in diagnosing PCa [47, 48].

Annexin A3 is a protein that is being studied as a possible biomarker for prostate cancer in urine. It belongs to a family of proteins known as phospholipid-binding proteins and shows altered expression in PCa [49].

α-Methylacyl coenzyme A racemase (AMACR) is an enzyme responsible for beta-oxidation of branched-chain fatty acids found in a diet consisting of beef and dairy products. Recent studies have shown that 88% of prostate carcinomas, as well as untreated metastases and hormone-refractory PCa, overexpress AMACR [50]. Immunohistochemical studies have shown that expression of AMACR in prostate tissue has a sensitivity of 97% and a specificity of 100% for the detection of PCa. In conjunction with other markers such as the tumor protein p63, which helps to identify basal cells that are absent in prostate cancer, measuring the expression level of AMACR also can be used for the detection of prostate cancer [51].

Detection at an early stage not only improves the outcome but also reduces mortality in PCa. Although the discovery and use of PSA have revolutionized current PCa detection and treatment, it is not enough. Due to this stage, various molecular modifications or genetic alterations have overtaken the current maximum use of this tumor marker. The use of different PSA derivatives, the discovery of molecular derivatives of PSA, new kallikrein markers, PCA3, and gene rearrangements are leading to a significant improvement in the efficiency of PCa management [24, 25].

#### **1.3 Urothelial cancer (UCa)**

The urothelium extends from the renal pelvis to the urethra of the prostate. Urothelial carcinomas (UCa) represent the vast majority of cancers arising in the bladder, and approximately 75% of them are noninvasive within the muscular layer. However, despite the treatment options, there is a high rate of recurrence and, in high-grade tumors, progression to muscle-invasive disease (**Figure 2**). The incidence of UCa increases with age. Most of them are diagnosed in patients over 65 years of age, and it is four times higher in men than in women. One of the reasons for this could be tobacco use, which is known to be a risk factor and is most common in men, although other factors such as the androgen receptor could also play a role [52, 53].

UCa can present a noninvasive phenotype, in which the malignant cells are confined to the urothelial layer, and an invasive phenotype, in which the tumor cells can

break through the basement membrane and invade the subepithelial connective tissue and underlying muscle. There are two types of noninvasive UCa. Exophytic papillary (Ta) tumors are prone to local recurrence but rarely break through the basement membrane or spread. CIS, on the other hand, is a flat lesion with a high susceptibility to invasion and metastasis. Patients who have only CIS lesions in their urinary system are more likely to develop synchronous and/or metachronous malignancies [54]. Ta tumors are caused by molecular abnormalities that are usually separate from CIS and invasive carcinomas, despite the fact that these pathways are not mutually exclusive [55]. The receptor tyrosine kinase-Ras pathway is frequently constitutively active in low-grade papillary carcinomas, with activating mutations in HRAS and FGFR3 [56]. Homozygous deletion of p16INK4a is a common feature in high-grade Ta tumors [57]. TP53 and retinoblastoma (RB) genes and pathways are commonly altered in CIS and invasive malignancies [58]. Although chromosomal-9 deletions can be observed in both dysplastic urothelium and CIS lesions, loss of chromosome 9q heterozygosity is more common in low-grade Ta tumors [59]. When a papillary tumor develops into an invasive phenotype, it is mainly due to the accumulation of additional mutations in the p53 pathway. Invasive cancers have also been shown to have p16 mutations. Matrix metalloproteinases (MMPs), cadherins, TSP-1 (thrombospondin-1), and vascular endothelial growth factors (VEGFs), mutations that alter the extracellular matrix and induce tumor angiogenesis are more common in muscle-invasive cancers and also play a role in nodal metastasis [60].

The most intensively studied aspects of UCa are changes in signaling pathways that affect cell cycle progression. The p53 and Rb signaling pathways, which interact with apoptosis and intracellular signaling mediators, are primarily responsible for cell cycle control. The tumor suppressor gene TP53 is located on chromosome 17p13.1 and encodes the p53 protein. By activating p21WAF1/CIP1, the protein blocks cell cycle progression at the G1-S transition. Inactivation of TP53 and loss of its tumor-suppressive activity may be caused by mutations in the 17p allele [61, 62]. In invasive UCa, loss of

heterozygosity on chromosome 17 is associated with aggressive behavior. Mutations in the TP53 gene result in a protein that is resistant to ubiquitin-mediated degradation. Immunohistochemistry can detect increased intranuclear p53 accumulation as a consequence [63]. Multiple retrospective studies have found that accumulation of p53 in the nucleus is associated with poor prognosis in patients with UCa, particularly in those who have undergone radical cystectomy. From normal urothelium to superficial UCa, muscle-invasive cancer and metastatic lymph nodes, altered p53 expression has been observed to occur [64, 65]. Despite this evidence, the predictive function of p53 in the development and progression of bladder cancer is still debated, but what is certain is that a link between the accumulation of p53 in the nucleus and TP53 mutations has been demonstrated [66].

Mdm2 interacts with p53 in an autoregulatory feedback loop that regulates its activity. Increased p53 levels transactivate the promoter of MDM2, causing the translated protein to facilitate the destruction of p53 by the proteasome. MDM2 levels decrease when p53 levels decrease. In UCa, MDM2 amplification has been found to increase in frequency with tumor stage and grade [67, 68]. p14 inhibits the transcription of MDM2. p14ARF, one of two splice variants derived from the CDKN2A locus on chromosome 9p21, encodes the protein. Because the E2F transcription factor induces p14ARF, it serves as a link between the Rb and p53 pathways. The E2F transcription factor is sequestered by dephosphorylated Rb. E2F is produced when Rb is phosphorylated by cyclin-dependent kinases, leading to the transcription of genes important for DNA synthesis [69, 70].

In UCa, a decrease in Rb protein expression has been highlighted. Rb has been shown to be a predictive factor when combined with other cell cycle regulatory proteins. Cyclin/cyclin-dependent kinase complexes help phosphorylate Rb. CDKIs such as p21, p16, and p27, which act as tumor suppressors, cause negative control of cyclin-dependent kinases. Low levels of p27 have been associated with advancedstage bladder adenocarcinomas [69–71]. In bladder UCa, p27 mutations have also been associated with poor disease-free and overall survival. In UCa patients treated with radical cystectomy, a combined assessment of p53, p21, Rb, cyclin E1, and p27 has been shown to improve accuracy against each individual molecular marker, thereby improving risk stratification [69–71].

Apoptosis is a tightly controlled process involving a series of events that occur during normal development and in response to a series of stimuli, all leading to programmed cell death. Apoptosis can be triggered in two ways. The internal process is mediated by mitochondria, while the extrinsic system involves the activation of death receptors on the cell surface. Both pathways activate caspases that cleave cellular substrates and allow apoptosis. In urothelial carcinoma cell lines, tumor-specific expression of caspase-8 has been shown to induce apoptosis *in vitro* [71].

The Bcl-2 protein family comprises both antiapoptotic and proapoptotic members, such as Bcl-2, Bax, and Bad, and is involved in the intrinsic apoptotic process. In UCa patients treated with radiotherapy or synchronous chemoradiotherapy, increased Bcl-2 expression has been associated with poor outcomes. In patients with advanced UCa undergoing radiotherapy who might benefit from neoadjuvant chemotherapy, Bcl-2 could serve as a marker [72, 73]. Expression of Bcl-2 has been associated with a lower tumor-free survival rate in high-grade T1 tumors, and in combination with p53, it may be a strong prognostic indication in non-muscle-invasive UCa. In addition, a prognostic index based on Mdm2, p53, and Bcl-2 was developed, with abnormalities in all three markers corresponding to the lowest probability of survival in UCa [74, 75]. Bax expression, on the other hand, is an independent predictor of better prognosis

#### *Urologic Cancer Molecular Biology DOI: http://dx.doi.org/10.5772/intechopen.101381*

in invasive UCa. The proapoptotic function of Bax is mediated by the activation of Apaf-1. In UCa patients, lower Apaf-1 expression has been associated with a higher mortality rate [76, 77].

Multiple cell-surface receptors modify signals from the environment and transmit them to the nucleus of urothelial cells via transduction pathways. Uncontrolled cellular proliferation and tumor growth may result from alterations in these receptors and/ or the signals sent. Activating mutations of FGFR3 are the best-studied alterations in UCa in the FGFR family. FGFR3 mutations are found in nearly 60–70% of low-grade papillary Ta tumors [78].

ErbB-1 and ErbB-2 (Her2/neu), members of the epidermal growth factor receptor (EGFR) family, are overexpressed in invasive UCa. Overexpression of ErbB-1 has been associated with an increased risk of progression and mortality [79]. Increased ErbB-2 expression has also been associated with aggressive UCa as well as poor disease-specific survival. In contrast, other studies have found that ErbB-2 expression is not related to prognosis. While it has been suggested that the combined expression profile of ErbB-1 and ErbB-2 is a stronger predictor of prognosis than either marker alone, this finding remains to be confirmed [80–82].

JAK (Janus kinase) is a tyrosine kinase that is activated by cytokines and growth receptors and regulates a variety of signaling pathways. JAK signaling is thought to be increased by overexpressed preoperative plasma levels of interleukin-6, a ligand for the corresponding cytokine receptor, and is an independent predictor of UCa recurrence and survival [83]. The activation of the STAT (signal transducer and activator of transcription) pathway, which controls transcription of several key genes, is the most studied molecular event after JAK activation. STAT1 inhibits Bcl-2 expression, whereas STAT3 has the reverse effect. In UCa patients, STAT3 expression in combination with other markers can predict the likelihood of recurrence and survival [84].

Angiogenesis is the process of cancer cells producing substances that interact with stromal components to recruit endothelial cells to the site of cancer and generate a vascular supply that gives cancer cells with the nutrients they need to proliferate [85, 86].

VEGFs are signaling proteins that stimulate angiogenesis by interacting with VEGF receptors and stimulating cellular responses (VEGFRs). The majority of known cellular responses to VEGF are mediated by VEGFR2. Advanced UCa and muscle invasion are linked to VEGFR2 expression. In UCa patients, VEGFR2 expression is also a key determinant of nodal metastasis. VEGF boosts nitric oxide synthase, which boosts nitric oxide production and tumor vascularization. In nonmuscle-invasive UCa, VEGF overexpression is linked to early recurrence and progression [87, 88]. VEGFs are signaling molecules that promote angiogenesis by interacting with VEGF receptors and stimulating cellular responses (VEGFRs). The majority of known cellular responses to VEGF are mediated by VEGFR2. Advanced UCa and muscle invasion are linked to VEGFR2 expression. In UCa patients, VEGFR2 expression is also the main predictor of nodal metastasis. VEGF boosts nitric oxide synthase, which boosts nitric oxide production and tumor vascularization. In nonmuscle-invasive UCa, VEGF overexpression is linked to early recurrence and progression [89, 90].

The ability of urothelial cancer cells to invade blood vessels and lymphatics is essential to their ability to spread to nearby structures and form distant metastases. Cadherins are intercellular adhesion mediators that have been identified in a variety of tissues. E-cadherin is the most known member of the cadherin family and is essential for epithelial cell adhesion. In UCa, lower E-cadherin expression has been linked to an increased risk of tumor recurrence and progression and shorter survival [91, 92]. The action of various protease families, including uPAs and MMPs, enhances the ability of a tumor to degrade the matrix and infiltrate the basement membrane. Thymidine phosphorylase (TYMP), an enzyme that increases MMP synthesis, is overexpressed in advanced UCa compared with superficial tumors or normal bladder tissue [93]. Increased thymidine phosphorylase nuclear reactivity has been associated with an increased prevalence of superficial UCa recurrence. MMP-2 and MMP-9 expression levels have been shown to be associated with the stage and grade of urothelial tumors. Increased MMP-2 expression may also indicate a poor prognosis for recurrence-free and disease-specific survival. In UCa patients, the ratio between MMP-9 and E-cadherin is a predictive factor for disease-specific survival [94, 95].

Integrins are transmembrane glycoproteins that can promote tumor development, invasion, and metastasis when their function is disrupted. They are protein receptors for adhesion molecules and collagen. The immunoglobulin superfamily member intercellular adhesion molecule 1 (ICAM1) interacts with particular integrin classes. ICAM1 expression is linked to an infiltrative histological phenotype, according to immunohistochemical investigations. The presence, grade, and size of bladder tumors have all been linked to serum ICAM1 levels [96].

In patients with UCa of the bladder, ICAM1 is part of a multimarker model that can predict nodal status. The α6β4 integrin is tightly connected to collagen VII in normal urothelial cells and inhibits cell migration. Superficial UCa has shown loss of polarity of α6β4 expression, and invasive tumors reveal either loss of α6β4 and/or collagen VII expression or lack of colocalization of either protein. Patients who have malignancies with weak α6β4 immunoreactivity have a better prognosis than those who have tumors with no or significant overexpression. Overall, molecular invasion indicators are relatively accurate predictors of outcome in UCa patients [97].

Circulating tumor cells are the most basic blood-based biomarker. The presence of tumor cells in the blood has been associated with advanced disease stages in various solid organ cancers. In a recent study, the predictive value of the amount of circulating tumor cells obtained with CellSearch technology was investigated in 100 UCa patients who had undergone cystectomy. About 25% of clinically localized Uca patients had circulating tumor cells, the researchers found, and they associated the results with a worse outcome for these patients [98].

Ki-67 is a nuclear protein that is synthesized by proliferating cells that is used to determine the percentage of cell growth fraction. In patients with superficial UCa, the cell proliferative index is associated with prognosis, and the Ki-67 antigen is a strong predictor of progression, recurrence, and treatment response. This result was confirmed in patients receiving cystectomy who had muscle-invasive UCa [99, 100].

Survivin is also an apoptosis inhibitor that can bind caspases after their activation and prevent them from cleaving their substrates. Survivin expression has been shown to be associated with bladder cancer progression and mortality, and its function as a prognostic indicator has been externally validated. In a multiplex panel including other apoptosis-regulating genes, survivin has been shown to predict tumor recurrence after cystectomy and mortality more accurately than clinicopathological factors alone [101].

COX-2 is an enzyme known primarily for being a target for nonsteroidal antiinflammatory drugs. Increased levels of COX-2 have been studied in both the upper and lower urinary tract as a marker of UCa angiogenesis and tumor aggressiveness. COX-2 was increased not only in upper urinary tract carcinomas but also in nearby nontumor cells (stromal cells), suggesting an association between more aggressive upper urinary tract malignancies and worse prognosis [102, 103].

IGF (insulin growth factor) and IGFBP-3 (insulin growth factor binding protein-3) are circulating proteins that function as growth signal mediators and mitogens, respectively. IGF and IGFBP-3 levels were measured preoperatively in individuals having cystectomy to see if they may be used as blood-based predictors of UCa outcome. Although individual marker levels were not efficient, an association between the two of them (low IGF-adjusted IGFBP-3 levels) was a predictor of distant metastases and poor survival [104].

Periplakin is a protein that is found in normal cellular desmosomes and is encoded by the PPL gene. In a cohort study of UCa patients, serum circulating periplakin was investigated and compared to 30 healthy subjects. While UCa patients had considerably lower serum periplakin levels than controls, this difference was diminished in patients with invasive tumors [105].

Bladder cancer is being more recognized as a disease that cannot be treated merely based on pathologic staging; instead, therapeutic efforts must focus on molecular abnormalities in particular tumors. The formation and course of urothelial malignancies have been better understood, thanks to the availability of advanced molecular profiling and computational methods. Future Uca treatment will rely on consensus marker panels to provide accurate prognosis and therapeutic response predictions in individual patients. The disease will be effectively treated if patients are stratified based on risk factors and tumor expression signatures, followed by optimum surgical treatment and disruption of important signaling pathways through the use of therapies targeting several molecular pathways [106].

#### **1.4 Kidney cancer**

Kidney cancer is the fourteenth most prevalent cancer in the world, with men having the ninth most common case and women having the fourteenth most common incidence [107, 108].

Renal cancers in adults include malignant tumors of the renal parenchyma and pelvis, but benign tumors and inflammatory causes should also be considered in the differential diagnosis of a renal mass. The majority of tumors arising from the renal pelvis are urothelial tumors, which account for less than 10% of all renal carcinomas. Renal cell carcinoma (RCC), also known as renal adenocarcinoma, is far more common than benign tumors or other malignancies and accounts for 90% of all kidney cancers. RCC can be divided into several histological subgroups, each with its own clinical features and evolution [109].

The clear cell type of renal cell carcinoma is the most common, accounting for 75% of new cases, followed by the papillary, chromophobe, medullary, and collecting duct subtypes, which account for 10%, 5%, 1%, and 1% of new cases, respectively [107].

Von Hippel and Lindau characterized a vascularized developmental pattern of the retina that was later identified as part of an autosomal dominant disease. Hemangioblastomas, pheochromocytomas, and clear cell renal carcinomas were also common in these patients. Up to 90% of sporadic RCCs have somatic mutations, promoter methylation, or loss of heterozygosity of VHL. The VHL protein is known to function as a substrate recognition component of an E3 ligase and for ubiquitination and degradation of HIF (hypoxia-inducible factors) [110–112].

The alpha subunit of HIF heterodimerizes with HIFβ under hypoxic conditions or in the absence/inactivation of the VHL protein, translocates to the nucleus, and transcribes a variety of genes, including VEGF, PDGF, and TGF. In most spontaneous RCCs, inappropriate activation of this system is a major cause of angiogenesis, invasion, and metastasis [113].

In metastatic or unresectable RCC, targeting the VEGF pathway has been a cornerstone of treatment. In metastatic RCC, small molecule TKI (tyrosine kinase inhibitors) have proven successful in interrupting VEGF signaling, resulting in longer patient survival. Endothelial cells can be stimulated to proliferate and migrate by VEGF and PDGF [113].

The development of an increased blood supply can promote the development of metastatic niches and lead to the spread of the tumor. Because of this significant metastatic potential, no neoadjuvant systemic treatment is currently accepted for RCC with targeted therapies such as sunitinib or pazopanib. These agents are also not approved for adjuvant treatment after nephrectomy. Several studies have failed to demonstrate that adjuvant TKIs or immunotherapies improve survival after definitive surgery, underscoring the need for early intervention with surgery upfront. The most common genetic mutation in RCC is the loss of chromosome 3p. This region contains the PBRM1 gene in addition to VHL (3p21) [114].

PBRM1 is a "gatekeeper" gene that helps in DNA repair, replication, and transcription. Somatic mutations have been detected in 41% of clear cell renal carcinomas, with estimates ranging from 40 to 50%. Loss of PBRM1 is associated with advanced disease stages, a higher grade cancer, and poorer treatment outcomes [115–117].

Mutations on chromosome 3p could indicate an important genetic event, whether inherited or acquired, that drives early carcinogenesis. RCC features a number of genomic changes, including an increase in 5q with TGFB1 and CSF1R, as well as a 14q deletion with the tumor suppressor candidate NRXN3. Loss of 14q has been related to a progression of the disease and a reduced life expectancy [118, 119].

mTOR is a serine/threonine kinase that forms two different complexes with adaptor proteins—mTORC1 and mTORC2. mTORC1 activity was found in more than half of all RCCs. HIF-1α has been demonstrated to promote the expression of REDD1, a proven mTORC1 inhibitor. Stabilization of HIF1 levels under hypoxic environments causes mTOR signaling to be inhibited [120].

Mutations in TSC1 and PTEN may prevent the HIF-1 signaling axis from inhibiting mTOR, resulting in a second, independent mechanism of carcinogenesis. Everolimus as shown in **Figure 3**, suppresses the activity of mTORC1 via binding to FKBP-12 [121, 122].

**Figure 3.** *Current therapeutic management of RCC.*

#### *Urologic Cancer Molecular Biology DOI: http://dx.doi.org/10.5772/intechopen.101381*

Over the past two decades, research has focused on molecular events that can uncover the biological heterogeneity underlying the diverse clinical behavior of RCC, with the expectation of identifying accurate markers that can personalize prognosis and risk-stratified clinical management, as well as predict response to existing therapeutic approaches [123].

Molecular biomarkers are associated with clinical and/or pathologic characteristics of RCC and have an effect on progression-free survival, OS, cancer-specific mortality, and prognosis [124].

In addition, new research has recently been published on PBRM1 (polybromo 1), BAP1 (BRCA1-associated protein 1), and SETD2 (SET domain-containing protein 2). Although these biomarkers are targeted by a variety of RCC treatments, their prognostic and predictive value has yet to be confirmed internally and externally. All tyrosine kinase inhibitors, such as bevacizumab, target VEGF, while some others, such as cabozantinib, target a larger variety of receptors, including AXL and the protooncogene c-met [123].

As explained earlier, VHL is responsible for the degradation of HIF-α. As a result, changes in VHL proteins lead to HIF-α accumulation in addition to hypoxic cell conditions. HIF-α is a key player in cancer pathogenesis, activating approximately 30 genes involved in tumor proliferation and angiogenesis, including the overexpression of VEGF. When ccRCC (clear cell renal cancer) is compared with papillary or chromophobe forms of RCC, HIF-α expression is significantly higher. In both clear cell and papillary RCC, studies have reported no difference in survival between patients with low or high HIF-α expression, while other studies have found a worse prognosis with increased HIF-α in cancer cells [123].

VEGF is a dimeric glycoprotein that promotes tumor growth and metastasis by influencing angiogenesis in both normal and pathological situations. Due to HIF-α dysregulation and hypoxia caused by an inadequate blood supply in larger tumors, VEGF production is increased in ccRCC. RCC patients with VHL gene mutations and advanced tumor grade have higher VEGF levels and secretion. VEGF expression correlates with tumor necrosis, microvessel invasion, tumor stage, and Fuhrman grade in ccRCC, in addition to tumor grade and size. In studies, increased VEGF levels have been observed to decrease progression-free and overall survival rates of RCC [123, 125].

The additional value of VEGF, despite its promising properties, has yet to be confirmed and externally validated. C-met is a receptor tyrosine kinase and a protooncogene. Angiogenesis, tissue regeneration, cell proliferation, and differentiation are controlled by this protein. Mutations in the c-met signaling pathways have been linked to a variety of tumors, including all forms of RCC [123, 125]. The upregulation of c-met has been linked to the VHL mutation in ccRCC. It has been found that c-met expression is particularly high in tumors with papillary and sarcomatoid differentiation. In recent studies, increased c-met expression was found to reduce cancerspecific mortality. Further research is required to fully understand the role of c-met in the etiology of RCC [126, 127].

Transmembrane protein CAIX is linked to tumor development, poor prognosis, and aggressive phenotype. CAIX is thought to be involved in the regulation of the tumor microenvironment, particularly the fluctuations in intracellular and extracellular pH in response to hypoxia in the tumor, and is regulated by HIF. CAIX is expressed in more than 80% of RCC samples and 90% of ccRCC samples and can therefore be used to confirm the diagnosis of RCC. CAIX expression has been associated with better prognosis and survival in patients with localized RCC and mRCC,

and with an inverse relationship with metastatic spread. In contrast, low CAIX expression was not associated with renal cancer mortality [128].

CAIX may be more useful in identifying small renal tumors. With the advancement of technology, three genes have been discovered to be altered in more than 10% of sporadic clear cell RCC: BAP1, PBRM1, and SETD2. We can assume that these genes play an important role in renal cell carcinoma because, as in VHL, they are tumor suppressor genes with two hits found on the short arm of chromosome 3p [129].

The mTOR pathway regulates cell proliferation, protein degradation, and angiogenesis as part of the biological response to environmental stress. PTEN (phosphatase and tensin homolog) is an upstream molecule in this process, while phosphorylated S6 ribosomal protein is a downstream molecule. The use of temsirolimus, an mTOR inhibitor, as a first-line treatment for low-risk patients is recommended in recent treatment guidelines. In addition, recent studies have shown that altering regulators of the mTOR pathway improves the accuracy of prognostic models as well as the ability to predict recurrence in ccRCC patients who had undergone nephrectomy [130, 131].

pS6 (ribosomal protein S6) is a downstream mTOR target that has been associated with the activation of the mTOR pathway. Due to phosphorylated pS6 activity, it exhibits S6 kinase activity that affects mRNA translation. PS6 is overexpressed in clear cell mRCC and could be used to predict survival in both localized and nonlocalized mRCC. pAkt (protein kinase B) regulates both growth and survival mechanisms by phosphorylating substrates in the cytoplasm and nucleus. Elevated pAkt is associated with lower RCC-specific survival, higher grade, and faster progression of metastasis [123, 132]. Overexpressed pAkt, on the other hand, was linked to a better prognosis in localized RCC. According to recent studies, the localization of pAkt may be essential in defining tumor behavior and thus prognostic value. They discovered a higher level of nuclear pAkt in localized RCC tissue than in mRCC tissue [123, 133].

The tumor suppressor protein PTEN is encoded by the tumor suppressor gene PTEN and is located upstream of mTOR. Via PI3K, PTEN inhibits the phosphorylation of pAkt. PTEN mutation is uncommon in renal cancer and is linked to a high mortality rate. PTEN expression is observed in cancers with a lower T stage and a nonclear cell histological subtype and increases survival [123, 133].

CAF (cytokine and angiogenic factors), survivin, caveolin-1, p53, vimentin, insulin-like growth factor II mRNA-binding protein 3, matrix metalloproteinases, fascin, ki-67, tumor necrosis, and c-reactive protein are examples of other biomarkers. Survivin is a member of the family of apoptosis inhibitors that are active in both the intrinsic and extrinsic caspase pathways [132]. It regulates mitotic progression and promotes alterations in gene expression associated with tumor cell invasiveness. Survivin mRNA is typically expressed during embryonic and fetal development and then disappears in most differentiated adult tissues. Survivin is overexpressed in a variety of malignancies, including all forms of RCC [123].

Given the importance of deregulation of apoptosis in carcinogenesis, it is not surprising that high expression of survivin is associated with poor differentiation, aggressiveness, and lower survival in ccRCC. The p53 protein is a DNA-binding molecule involved in transcription and the regulation of cell growth. When DNA damage occurs, p53 initiates apoptosis and causes cell cycle arrest. Overexpression of p53 has been found in all forms of RCC, especially papillary RCC. Although p53 has been shown to be an independent predictor of metastasis-free survival in patients with localized clear cell RCC, its prognostic significance in RCC is still debated [123].

#### *Urologic Cancer Molecular Biology DOI: http://dx.doi.org/10.5772/intechopen.101381*

MMPs are overexpressed in all forms of RCC cancer, especially in nonclear cell RCC tumors, and are associated with aggressive behavior, tumor grade, and survival. Batimastat (synthetic) and bryostatins (natural) are MMP inhibitors that may help to treat and prevent MMP-overexpressing malignancies [123].

IMP3 (insulin-like growth factor II mRNA-binding protein 3) is an RNA-binding protein found in oncofetal tissues. Insulin-like growth factor II mRNA transcription is regulated by it. IMP3 is expressed during embryogenesis in a variety of developing tissues, including epithelium, muscle, and the placenta. In adult tissues, however, it is expressed at low or undetectable levels. In several malignancies, including RCC, IMP3 is associated with cell proliferation and invasion. Stage, grade, sarcomatoid differentiation, regional lymph node involvement, distant metastasis, and cancerspecific mortality are all associated with IMP3. The inclusion of IMP3 expression in the tumor stage increases metastatic progression prediction of metastatic progression and the predictive value of IMP3 in ccRCC was externally validated by researchers [123, 134, 135].

Ki-67 is a cell proliferation marker that has been linked to higher recurrence rates, a more aggressive phenotype of ccRCC, and a poor prognosis. The combination of Ki-67 and CAIX improves the predictive power of nuclear grade in assessing cancerspecific mortality. Complementary studies are needed to evaluate its significance as a prognostic factor [123].

Caveolin-1 is a structural component of caveolae, microdomains of the plasma membrane that regulate cell adhesion, growth, and survival through intracellular signaling. Caveolin-1 is detected in 86% of ccRCCs and 5% of chromophobe and papillary RCCs. The caveolin-1 expression has been linked to a poor clinical outcome in a variety of cancers [123].

One of the components of the scoring algorithm of Leibovich et al. is tumor necrosis. The importance of this component in the prognosis of RCC has led to some debate. When typical clinical and/or pathological tumor features were considered, several studies found that tumor necrosis had no additional value. In contrast, Lam et al. found that tumor necrosis improved survival prediction in patients with localized RCC [136, 137].

The inflammatory marker C-reactive protein has been shown to be a significant predictor of metastasis and overall mortality after nephrectomy for localized renal cell carcinoma. It improved the predictive accuracy of a number of known clinical and pathological predictors by up to 10%. Karakiewicz et al. studied and stated CRP as an independent predictor of mortality in RCC [138].

They also discovered that CRP improved the accuracy of the UISS prediction model. According to Michigan et al., elevated CRP was associated with increased mortality in patients undergoing nephrectomy. Erythrocyte sedimentation rate (ESR), another inflammatory marker, was also associated with higher all-cause mortality. Because they are affordable and readily available, these markers are very promising [123, 127].

Vimentin is a cytoplasmic intermediate filament that should not normally be detected in epithelial cells. Its overexpression has been observed in up to 51% of ccRCC and 61% of papillary RCC, and it has been associated with poor outcomes, regardless of T stage or grade [123].

Fascin is a globular actin cross-link protein that plays a role in cell motility and adhesion. Its overexpression has been associated with sarcomatoid tumors, their stage, grade, size, and metastatic ability [123].

CTLA-4 is a protein described on the surface of cytotoxic T lymphocytes. It is thought to reduce inflammation by preventing tumor-infiltrating lymphocytes (TILs) and T cell activation by preventing tumor cell B71 from binding to CD28. The presence of CTLA-4 has been associated with higher tumor grade in RCC. Lymphocytes also have a cell surface receptor, PD-1. It belongs to the immunoglobulin family and binds to the ligands PD-L1 and PD-L2, which are found on almost all cells, including tumor cells. They are thought to promote apoptosis by decreasing the activity of cytotoxic T cells [139]. In addition, tumor cells are thought to express PD-L1/B7-H1 to prevent tissue destruction by an activated immune system [123, 127].

PD-1 inhibitors, particularly nivolumab, have been the subject of numerous studies, all of which have yielded promising results. The FDA approved nivolumab as second-line therapy for RCC in 2015, based on the results of a study that OS showed benefit, good tolerability, and improved health-related quality of life with nivolumab treatment. It is worth noting that ongoing trials using a mix of targeted treatments, such as anti-VEGFs, and nivolumab are showing promising results [122, 123].

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Pavel Onofrei1 \*, Viorel Dragoș Radu<sup>2</sup> \*, Alina-Alexandra Onofrei3 , Stoica Laura1 , Doinita Temelie-Olinici1 , Ana-Emanuela Botez1 , Vasile Bogdan Grecu1 and Elena Carmen Cotrutz1

1 Cell and Molecular Biology – Deparment of Morphofunctional Sciences II, "Grigore T. Popa" University of Medicine and Pharmacy, Iasi, Romania

2 Deparment of Surgical Sciences II (Urology), "Grigore T. Popa" University of Medicine and Pharmacy, Iasi, Romania

3 Emergency Hospital for Children "St. Mary", Iasi, Romania

\*Address all correspondence to: onofrei.pavel@gmail.com and vioreldradu@yahoo.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 3**

## Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components

*Ban Hussein Alwash, Rawan Asaad Jaber Al-Rubaye, Mustafa Mohammad Alaaraj and Anwar Yahya Ebrahim*

#### **Abstract**

The dynamic alterations in the cytoskeletal components actin and intermediate, etc. filaments are required for cell invasion and migration. The actin cytoskeleton is a highly dynamic structure that is governed by a delicate balance of actin filament formation and disassembly. To controlling the activities of key components of the epithelial mesenchymal transition (EMT) could be a viable solution to metastasis. Bioinformatics technologies also allow researchers to investigate the consequences of synthetic mutations or naturally occurring variations of these cytoskeletal proteins. S100A4 is S100 protein family member that interact with a variety of biological target. In study has shown that S100A4 interacts with the tumor suppressor protein p53, indicating that S100A4 may have additional roles in tumor development. The S100A4 and p53 interaction increases after inhibition of MDM2-dependent p53 degradation using Nutlin-3A. The main goal of this research was control of cytoskeletal dynamics in cancer through a combination of, actin and S100A4 protein. The investigate the molecular mechanism behind S100A4 function in (EMT) and indicating that S100A4 is promoting p53 degradation. Understanding the signaling pathways involved would provide a better understanding of the changes that occur during metastasis, which will eventually lead to the identification of proteins that can be targeted for treatment, resulting in lower mortality.

**Keywords:** cytoskeleton dynamics, actin, cancer, S100A4 protein and p53, bioinformatics

#### **1. Introduction**

A highly coordinated multistep process involving the stroma, blood vessels, and cytoskeleton is the leading cause of death in cancer. Invasion, migration, extravasation, and angiogenesis are all important factors in successful metastasis. Invasion is a limited process that occurs at the tumor-host interface, where tumor and stromal cells exchange enzymes and cytokines that modify local ECM and promote cell

movement [1]. The ability of cells to move and divide is controlled by dynamic changes. Most cancers are characterized by changes in the expression levels of numerous protein kinases. As a result, most cancer cells show dynamic alterations in cytoskeletal proteins. The capacity of cancer cells to divide, infiltrate, and generate distal metastases is complicated by their migratory nature, the plasticity of cell migration, and these dynamic alterations. The importance of dynamic alterations in the modulation of the function of various cytoskeletal polymers in cancer cells is highlighted in this work. Actin (which generates MF), myosin (mini-filaments), tubulin (MT), and several IF protein families, such as keratins, desmins, peripherin, vimentin, internexins, and others, are among these monomers [2]. The mesenchymal-toepithelial transition (MET) theory was established to explain these phenomena when histological examinations revealed that macrometastases have epithelial phenotypes rather than mesenchymal phenotypes [3]. DTCs undergo MET to transition from a mesenchymal to an epithelial form, allowing them to multiply at the metastatic site and develop into macrometastases, according to this view. The involvement of the actin cytoskeleton, microtubules, and intermediate filaments in EMT is explored in this paper, as well as how these cytoskeleton proteins can be exploited as a possible biomarker. The S100 family is a subgroup of calcium-binding proteins with EF-hands that regulate a number of cellular processes by interacting with a variety of protein targets. S100A4 expression has been found in fibroblasts, blood cells, and endothelial cells, and it is thought to be one of the mesenchymal cell markers involved in the epithelial-mesenchymal transition (EMT) [4, 5]. The capacity to migrate efficiently in cell motility experiments is a characteristic trait of S100A4-positive cells, but ectopic production of S100A4 in S100A4-negative cells increases migration [6]. Monomers of folded 10S and unfolded extended 6S versions of Nom-muscle myosin (NM IIA) protein exist. The latter has the ability to form filaments [7]. In cancer, genetic changes that impact protein kinases are quite common [8, 9]. Mutations or deletions that induce loss of function or enhanced catalysis are the most common. Activating mutations might have unanticipated consequences for several cytoskeletal systems. Mutations in the small GTPase RhoA, for example, may result in enhanced activation of proteins that regulate minifilament production [10].

These events result in abnormal molecular activities in cancer cells, such as enhanced cell motility, invasion, division, and mechanosensing. The occurrence of many isoforms of these proteins, some of which have non-overlapping activities, complicates the investigation of these alterations. Actin, tubulin, and myosin are all isoforms, and IF comes in a variety of forms and variations. One of the main goals of this project is to present a broad, although incomplete, view of the field. Finding possible areas that could be targeted specifically to treat a variety of cancers in human cancer A431 cells, we show that S100A4 expression is increased during EMT mediated by the transcription factor ZEB2. In addition, we show the interaction between endogenous S100A4 and p53 in cells and that the interaction takes place within the cell nucleus. We also show that knockdown of S100A4 results in stabilization of p53 at the protein level. Further, knockdown of S100A4 is shown to increase the transcriptional activity of p53, resulting in p53-dependent growth arrest [11]. Transgelin (TAGLN) has been shown to have a role in the genesis of proteinuria, although the mechanism by which it does so is unclear. The goal of this research was to look at the involvement of TAGLN in the development of proteinuria. The study's distinctive feature is that it provides an updated, birds-eye view of the global changes in the cytoskeleton, which includes changes in tubulin and intermediate filaments as well as actin and actin binding proteins.

*Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

#### **2. The cellular cytoskeleton's role in EMT**

The cytoskeleton provides the mechanical strength and integrity that allows cells to maintain their shape and movement. **Figure 1** depicts the situation. As seen in the first step, epithelial cancer cells undergo EMT, losing their cell-cell connections and gaining the potential to penetrate the surrounding tissue parenchyma. These EMTinduced cells can subsequently intravasate into the systemic circulation and survive in the circulation before reaching the target site in the third stage. The cells must then extravasate into the tissue parenchyma in the fourth phase before going into dormancy or becoming micro metastases. MET activation in the fifth phase is required for subsequent improvement and potentially life-threatening mega metastases.

The epithelial cytoskeleton is remodeled during EMT, resulting in cell polarity loss and extracellular matrix remodeling (ECM). The cells then become motile and have the ability to invade [12]. The cytoskeleton's critical function in the EMT process is described in the following sections:

#### **2.1 Cytoskeleton of actin**

Actin filament remodeling is linked to EMT [13], and it is one of the most important components of the cytoskeleton. G-actin (globular actin) is a monomeric unit,

*The metastatic cascade is represented by the EMT-MET model.*

while F-actin (fibrous actin) is a polymeric filament. G-actin is distributed uniformly throughout the cytoplasm and nucleus. With the simultaneous hydrolysis of ATP, G-actin rapidly polymerizes to create F-actin under specific physiological conditions. Actomyosin mediates cell spreading and adherence to the ECM by producing conspicuous bundles of F-actin known as stress fibers. Stress fibers attach to focal adhesions and have a function in cell adhesion and morphogenesis as a result. Within the leading cell edge, actin filaments engage with actin-binding proteins and myosin II to deliver F-actin. For cell migration, this is a crucial process. Through its ATP-dependent motor activity, myosin II is thought to play a key role in the construction and disassembly of the actin cytoskeleton [14]. Different biological activities such as cell motility, cell shape, and so on rely on actin organization [15]. Gene expression, post-translational protein modification, and cytoskeleton remodeling all play a role in the EMT process [16]. Recent research has discovered that cells in intermediate phases of EMT have increased tumor-cell spreading ability. E-cadherin complexes have also been demonstrated to be connected to the dynamic actin framework via -catenin and stabilized by inhibiting RhoA activity and activating Rac and cdc42 [13, 17]. Cell-surface receptors, such as integrins, bind to ECM components and play a vital role in altering cell attachment, which is necessary for motility and invasion. A multi-protein complex binds to the actin cytoskeleton and achieves integrin-mediated cell-matrix adhesion.

#### *2.1.1 Proteins that bind to actin*

The actin cytoskeleton is made up of actin microfilaments and a large number of actin-binding proteins (ABPs). ABPs are proteins that regulate the formation and disassembly of actin microfilaments. This is important for cell motility, division, and cancer growth, all of which require coordinated actin filament turnover and remodeling [18]. Actin filaments are grouped in a loosely ordered meshwork in lamellipodia, which is referred to as dendritic networks [19], whereas actin filaments are arranged in parallel bundles in filopodia [20]. The action of specific actin-organizing proteins is required for these two types of organizations. During migration, the depolymerization of actin and debranching allows for the dynamic remodeling of the actin network as well as the cyclic extension and retraction of lamellipodia, which generates the pushing force that propels the cell forward. The cell body follows the orientation of the front lamellipodia due to the contraction of actin filaments. Filopodia are made up of closely packed parallel actin filaments with tapered ends facing the plasma membrane. Small crosslinking actin-binding proteins like fascin are principally responsible for bundling filopodia filaments [13, 21].

Cells are thought to be able to penetrate the tissue barrier by forming invadopodia, which are F-actin protrusions that breakdown the ECM, allowing cell penetration [22]. Invadopodia are actin-rich protrusions that are engaged in cell penetration and are related with ECM degradation via local deposition of proteases. The Arp2/3 (actin-related protein2/3) complex is a seven-subunit protein that is regulated by the WAVE and WASP families of WH2 domain-containing proteins (WAVE1, 2, and 3, WASP, and N-WASP), which bind both the Arp2/3 complex and actin monomers [23]. Arp2/3 is a protein complex that aids in the polymerization of actin filaments. Arp2/3 is typically overexpressed in cancers such as breast and liver carcinomas, implying a link between dynamic actin rearrangement and cancer progression [24]. Cortactin, an actin-binding protein, also binds to Arp2/3, allowing active Arp2/3 complexes to be located on the sidewalls of existing actin filaments, resulting in branched arrays of F-actin. Cortactin overexpression has been discovered during metastasis [25, 26].

#### *Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

Facin, an actin-binding protein that stimulates the development of invadopodia and filopodia, is increased during migration [27]. Gelsolin is essential for the formation of lamellipodia and podosomes, both of which are critical protrusions for motile cells [28]. The actin nucleating proteins that regulate cell mobility and organization are known as formins. EMT has been shown to upregulate formin expression at the leading edge in mesenchymal-transformed cells [29]. The gene coding for ABPs has been found to have altered transcription or translation in several cancer types, according to studies. Because ABP expressions vary throughout cancer types, changes in the actin cytoskeleton are a common characteristic of tumor cells. In breast cancer tissues, ARPC2 (actin-related protein2/3 complex) expression is greater and ARPC2 expression is associated with EMT and metastasis [13, 30]. Filamin deficiency has been found to be prevalent in carcinomas such as colon, prostate, and breast cancer [31]. As a result, migration is boosted, which is linked to a bad prognosis [32]. Higher levels of-actinin (actin filament cross-linker) are linked to a bad prognosis in breast cancer, as well as the degree of clinical progression and lymph node status [33].

#### **2.2 Rho GTPases**

Rho GTPases play a role in a range of cellular activities, including cell migration, cell polarity, and cell cycle progression, by controlling actin, MT dynamics, and regulating cytoskeleton and cell adhesion dynamics. It has been established that increased expression of Rho GTPases genes associated with a metastatic phenotype in a variety of cancer types, and are tightly related to the actomyosin cytoskeleton's overall control [34]. Rac1, RhoA, and Cdc42 are members of the Rho family of GTPases, which regulate actin cytoskeleton organization such as cytoskeletal dynamics, cell-cell junction assembly/disassembly, and integrin-matrix adhesion. Controlling the activities of Rho GTPases is critical during the growth-factor-induced EMT. Rho signaling activity is controlled by guanine nucleotide exchange factors (GEFs) which catalyze the exchange of GDP to GTP. During growth factor-induced EMT, controlling the activities of Rho GTPases is crucial. Guanine nucleotide exchange factors (GEFs), which catalyze the conversion of GDP to GTP, regulate Rho signaling activity. GTPase-activating proteins (GAPs) facilitate intrinsic GTPase activity to re-form the GDP bound state, which inactivates Rho action. Finally, the inactive GTPase domains and their covalently linked lipid groups engage with the guanine nucleotide dissociation inhibitors (GDIs). As show in **Figure 2**, the GDIs prevent GDP from being dissociated from Rho GTPases, which could inhibit spontaneous activation [35].

Rho GTPase activity in cells is regulated by Rho-dependent factors, as shown in this diagram. GEFs can stimulate Rho-GTPases to engage with downstream actomyosinregulating effectors by activating the exchange of GDP for GTP, whereas GAPs bind to the GTPase and boost the intrinsic GTPase activity by switching bound GTP to GDP. The GDIs interact with the GDP-bound version of the molecule, preventing GTP binding and thus activation. This illustration is based on Raftopoulou and Hall [36]. Rho GTPases function as molecular switches that cycle between a GDP-bound inactive form and a GTP-bound active form to govern signal transduction pathways [13, 36].

Rho governs cytoskeleton alterations and stimulates actin stress fiber production, impacting cell-cell or cell-matrix adhesion. Rho signaling is important in the regulation of actin-myosin contraction because it stimulates actin reorganization, which leads to the formation of stress fibers. Many of these regulatory mechanisms become unregulated in cancer cells, which contributes to invasive behavior during metastasis, according to recent research [37].

**Figure 2.** *The diagram depicted Rho GTPase cycle.*

#### **2.3 Microtubule (MT)**

In EMT, all aspects of the actin cytoskeleton and intermediate filaments are well identified, but the function of microtubules (MT) is still being explored. MTs are an important part of the cytoskeleton and play an important role in movement, intracellular transport and supporting cell shape [38]. MTs are composed of α and β-tubulin dimers, which mostly grow and shrink from the positive end and produce dynamic instability [39]. The function of MTs depends on their assembly and stability, which are regulated by post-translational modifications and interactions with various stable and destabilizing proteins [40]. Calmodulin regulated spectrin associated protein (CAMSAP3) is an MT-binding protein required to maintain MT tissue. It has been shown that the loss of CAMSAP3 promotes Akt dependent EMT through tubulin acetylation [41]. Studies have shown that the microtubule-interacting protein EB1 (end-binding protein) is located in one location and interacts with the microtubules. EB1 is a negative regulator of microtubule stability and promotes the migration of tumor cells. It modulates the dynamics of MT both in vitro and in vivo [42, 43]. Stathmin is an MT regulatory protein that depolymerizes MT and strengthens and regulates MT dynamics. MT destabilization is related to the phosphorylation of stathmin at its four serine residues [44]. In some human cancers, such as Wilms' sarcomas and tumors, stathmin levels have been elevated and have been associated with more aggressive metastases [45]. During EMT, MT plays a significant role in cell migration. Anti-MT drugs act on the one hand by inhibiting cell division, but also by inhibiting cell migration by stopping the formation of projections of MT-based membranes [46, 47]. Stability variability in MT regulates cortical F-actin by activating or inhibiting various Rho GTPases [13, 48]. Aside from their roles in cell division and migration, MT is also important for cell polarization. The creation of a polarized MT required for morphogenesis and cell migration is thought to be aided by cortical control of MT. Although MT indirectly contributes to cell-cell adhesion through

*Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

dynamic remodeling of the actin network, the role of MT in regulating migration or EMT by interacting with cell-cell adhesion is currently being investigated. Reveal that the MT-interacting protein stathmin is important in cell migration and metastasis via MT-actin cytoskeleton crosstalk [49]. Novel pharmaceutical techniques could be created using this relationship, in which the actin cytoskeleton is targeted via MT, to overcome the toxic effects associated with some actin-based medicines.

#### **2.4 Intermediate filament (IF)**

Intermediate filaments (IF) are important cytoskeletal components that provide structural support and mechanical strength. One of the largest gene families in the human genome encodes more than 50 different IF proteins, and this family contains five different IF classes. Types I-IV are located in the cytoplasm and include vimentin, which is a classic marker of EMT, and its expression is related to the aggressive phenotype of epithelial cancer. Compared with actin cytoskeleton and MT, IF also shows a different tissue expression pattern. Type I IF keratin is epithelial-specific and is essential for the mechanical stability of epithelial cells. During EMT, the reduction of keratin is generally considered to be the histological and biochemical characteristics of cancer cells [50, 51]. Type III IF, vimentin, is a typical marker of EMT. Vimentin expression is up-regulated during EMT of epithelial cells, and it has been reported to increase vimentin expression in various cancer cell lines. It is used as an indicator of poor prognosis [52]. During EMT, vimentin helps determine and maintain cell shape. Recent studies have shown that the expression of vimentin is related to active prostate cancer cell lines, and its knockdown significantly reduces the activity and invasiveness of tumor cells [13, 53]. It shows that vimentin is significantly increased in polyploid giant cancer cells (PGCCs). Vimentin intermediate filaments are responsible for expanding morphology and increasing migration [54]. In general, vimentin expression has significant characteristics during EMT, including tumor cell migration and invasion.

#### **3. Materials and methods**

The materials and methods are described in the following steps:

#### **3.1 Evaluation of S100A4 and p53 interaction in cells**

S100A4 interacts with p53 in the nucleus S100 family proteins have no known enzymatic activity, and therefore it is generally believed that S100 proteins function through interaction with other proteins to regulate their functions. Nuclear colocalization between S100A4 and p53 was however apparent both in untreated and cisplatintreated A549 cells [11]. Therefore, to investigate the suggested interaction between S100A4 and p53. IP of endogenous S100A4 in A549 cells resulted in coprecipitation of endogenous p53 in untreated cells. In addition, the amount of coprecipitated p53 increased after treatment of the cells with the p53-stabilizing drug Nutlin-3A **Figure 3**. To validate the interaction between S100A4 and p53 and to retrieve information about the subcellular location of the interaction, using antibodies targeting S100A4 and p53 **Figure 3**. The results from PLA supported the interaction between S100A4 and p53 in cells, and also underscored the dramatic increase in the interaction after treatment with Nutlin-3A. In addition, in situ PLA clearly showed that the subcellular location of the interaction between S100A4 and p53 was in the nucleus **Figure 3**.

#### **Figure 3.**

*Immunoblot analysis of p53 and S100A4 protein levels in A549 cells in response to Nutlin-3A treatment at indicated time-points.*

#### **3.2 Cytoskeleton protein transgelin developing proteinuria by bioinformatics**

The cytoskeleton protein transgelin is designated in the following phases:

#### *3.2.1 Immunity and TAGLN-related transcription factors (TFs) correlation analysis*

For stratification of the immune milieu based on function and activity, a group of important immune-related genes that have been widely researched in carcinogenicity were discovered. A scatter plot was used to display statistically significant genes in each category, as well as all relationships within each categorization.

#### *3.2.2 Analysis of the relationship between TAGLN and well-known genes involved in cell viability and apoptosis*

According to their function and activity, a group of well-known cancer genes that have been widely examined in carcinogenicity were gathered and divided into cell cycle-related and apoptosis-related pathways. The apoptosis-related star genes were divided into two groups: G0-G1 and G2-M. The expression profile data for each class was used to determine the associations between TAGLN and the star genes.

Differentially expressed genes (DEGs) were identified using Gene Expression Omnibus microarray expression profiling datasets and processed using the short time series expression miner to cluster DEGs in proteinuria progression and build a gene interaction network [55].

#### **3.3 Western blotting**

Western blotting dry was used to determine the quantity of extracted P53. In one input, the total protein extracted from cells was displayed, whereas flow-through indicated unbound protein (4-A) [11]. This method was chosen to avoid the presence of antibodies, which could cause more P53 aggregation. To conduct the negative staining experiment, recombinant S100A4 protein was purified under natural conditions. Luciferase IIA immunoprecipitated from A431/ZEB2-WT cells was analyzed using Western blotting [11]. Elution displays the amount of protein that separated from the immunocomplex, while beads reflect the immunocomplex. To see if p53 stabilization

*Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

alone has an effect on cellular S100A4 levels. Nutlin-3A prevents p53 from interacting with MDM2, the ubiquitin E3 ligase that ubiquitinates p53 and sends it to the proteasome for destruction. We were unable to identify any changes in the messenger RNA (mRNA) level of S100A4, indicating that the increase in S100A4 in response to Nutlin-3A was due to protein stabilization. Knockdown of S100A4 results in increased cisplatin-induced apoptosis S100A4 knockdown by itself did not induce apoptosis, but still the increased p53 levels could prime the cells for apoptosis activation.

#### **3.4 Microscopy with immunofluorescence**

Cells were cultured on 9 mm glass coverslips (VWR), fixed with 4% paraformaldehyde (VWR), and permeabilized with 0.5% Triton X-100 (Sigma). Primary and secondary Alexa Fluor conjugated antibodies (Life Technologies) were used for 1 hour of staining. Nuclear staining was done with DAPI (Sigma). An inverted Nikon Eclipse Ti microscope and a custom-built prism-based TIRF microscope with 60× objectives were used for confocal and TIRF microscopy [56]. Samples were analyzed with the help of sample.

#### **4. Result**

#### **4.1 In the nucleus S100A4 and p53 interaction**

S100A4 interacts with p53 in the nucleus because S100 family members have no known enzymatic activity, it is usually assumed that they control their activities via interacting with other proteins [11]. Non-muscle myosin IIA and p53 have already been identified as possible S100A4-interacting proteins. As a result, we started to look into the possible relationship between S100A4 and p53. In untreated cells, IP of endogenous S100A4 resulted in coprecipitation of endogenous p53.

In addition, as shown in **Figure 4**, the amount of coprecipitated p53 increased after the cells were treated with the p53-stabilizing medication Nutlin-3A. We used

**Figure 4.** *S100A4 interacts with p53 in the nucleus.*

antibodies targeting S100A4 and p53 to perform in situ PLA35 to confirm the interaction between S100A4 and p53 and to acquire information regarding the subcellular location of the interaction. PLA findings confirmed the contact between S100A4 and p53 in cells, as well as the substantial increase in the interaction following Nutlin-3A therapy. Furthermore, in situ PLA clearly demonstrated that the subcellular location of the interaction between S100A4 and p53 was in the nucleus as shown in **Figure 4**.

To utilizing cisplatin, a cytotoxic agent that promotes apoptosis in p53-dependent cells, to see if this was the case. We found higher cisplatin sensitivity in S100A4 shRNA cells relative to control cells using both a short-term cell viability assay and a clonogenic survival experiment as shown in **Figure 5**. We employed different assays to analyze cell mortality after S100A4 knockdown to learn more about the cisplatin response. S100A4 is significantly silenced as shown in **Figure 5**.

#### **4.2 The actin cytoskeleton in EMT: clinical evidence and therapeutic implications**

Recent research has revealed that scientists are concentrating their efforts on combination therapies that target numerous molecules in the same signaling pathway, multiple pathways in the same tumor, or both cancer cells and immune cells [57, 58]. Combination medicines are still being studied, and they will help us better understand drug resistance processes in the future. As a result, recent theories propose that targeting EMT and cytoskeletal proteins could be a unique way to battle cancer medication resistance. Normal cell physiology requires actin. As a result, despite their promise in vitro and in vivo, prospective actin-specific chemotherapeutics have yet to be tested. Due to their non-specific targeting of normal tissues, which causes cardiotoxicity and renal difficulties, they have not been successful [59, 60]. Increasing data suggests that the commencement of the EMT process and metastasis causes an increase in the number of EMT-related actin-binding proteins (ABPs) involved with actin cytoskeleton remodeling. As a result, controlling ABP expression may aid in preventing cancer cells from migrating and increasing their sensitivity to

**Figure 5.** *Knockdown of S100A4 results in increased cisplatin sensitivity.*

#### *Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

therapeutic therapies. Arp2/3, cortactin, formins, and fascin have all been studied extensively. Other ABPs, which could be potential targets in carcinogenesis, are, however, understudied. The actin cytoskeleton and ABPs are difficult to target for anti-cancer therapy, because ABPs are involved in the creation of contractile structures in cardiac and skeletal muscles [13, 61]. The intermediate filaments vimentin and nestin are linked to several cancers. When it comes to EMT, vimentin is a marker for mesenchymal cells. Anti-tumor medications have been discovered to change microtubule dynamics, which affect mitosis and apoptosis [62]. Microtubules have a big role in tumor migration and invasion during EMT. These anti-tumor medications stop cancer cells from dividing and forming membrane protrusions caused by network-based microtubules, which cause cell migration and invasion. Eribulin is a MI depolymerization medication that is used to treat metastatic breast cancer patients. In breast cancer, this medication suppresses angiogenesis, vascular remodeling, and EMT [63, 64]. The anti-tumor medication diaryloxazole PC-046 has a high oral bioavailability. It is a synthetically produced small molecule microtubule destabilizing agent. When compared to other microtubule destabilizing agents, this medication is reported to have a lower rate of MDR cross-resistance. Drug resistance in cancer cells is influenced by many signaling pathways involved in EMT and cytoskeletal proteins [65].

Anti-apoptotic effects and drug efflux pumps are increased in EMT cells. As a result, recent theories imply that focusing on EMT and cytoskeletal proteins could be a unique way to battle cancer treatment resistance. Chemotherapy is commonly used in the treatment of cancer, either alone or in combination with radiotherapy or surgery. Multiple breakthroughs in cancer treatment have been made in recent years, while medication resistance, which has been one of the leading causes of cancer death, has increased [66, 67]. In a drug-filled environment, EMT cells are thought to have the ability to develop selectively. While some studies imply that EMT may not totally contribute to cancer metastasis, others reveal that EMT is strongly linked to treatment resistance in cancer cells. Anti-microtubule drug resistance is thought to be caused by changes in the drug target, such as altered microtubule dynamics, tubulin mutations, modified tubulin isotype expression, and altered microtubule regulatory proteins, according to a large body of research. Other cytoskeletal proteins that can regulate microtubule regulation via signaling or structural links have also been discovered may be essential factors of anti-microtubule resistance [68, 69]. ADCs (antibody-drug conjugates) are a new type of targeted anticancer therapy that has been shown to be effective in MDR cancer. When a high-affinity antibody (Ab) binds with the drug and pushes a targeted drug delivery into the cell, this ADC causes apoptosis in tumor cells. In **Figure 6**, aside from producing a cytotoxic load paired with tumor cell death, this Ab-drug combination also blocks the cells' pro-survival receptor. The discovery of ADC could lead to the development of other combination medicines, such as immunotherapy. A lot of work is being done right now to improve the efficacy and targetability of ADCs in the treatment of cancers.

In **Figure 6**, (i) high-affinity antibody binds to the drug. ADC is formed when an antibody binds to a drug and enters the cell's double lipid-membrane layer, causing cell death. (ii) ADC attaches to a cancer cell's pro-survival receptor, blocking its function and triggering apoptosis. (iii) ADC binds to both the cancer cell's membranesurface antigen and an immune system effector cell, causing cancer cells to be lysed by cellular cytotoxicity.

**Figure 6.** *Diagram depicting the antibody-drug conjugate (ADC) mode of action in a cancer cell.*

#### **5. Conclusions**

It was necessary to conduct research. The plasticity of the cytoskeleton, motility, multi-drug resistance, and immunosuppressive properties have revealed a great deal about the plasticity of the cytoskeleton, motility, and immunosuppressive properties during the transformation of an epithelial cell to a mesenchymal cell. The cell's signaling systems, and how it adapts in order to live although there has recently been an emphasis on finding new cytoskeletal markers that can be used to detect cancer. Recent research suggests that cytoskeleton dynamics and EMT have a strong association, which can be used to find possible biomarkers. Epithelial cells lose their apical-basolateral polarity and adopt a fibroblast-like motility characteristic during EMT. S100A4 is a mesenchymal marker that is essential for improved mesenchymal cell motility. We chose to study the interactions between NMIIA and S100A4 in a cellular model of EMT because both proteins are expected to work together to generate the mesenchymal cell phenotype. There is less evidence for an S100A4-NMIIA complex in vivo. In this study, we report on control of cytoskeletal dynamics in cancer through a combination of actin and S100A4 protin. The interaction between S100A4 and p53 in the nucleus, and also that S100A4 negatively affects cellular p53 protein levels. In situ PLA was utilized to look at the interaction between p53 and S100A4. We were able to confirm not just the connection between S100A4 and wt p53, but also that it occurs in the cell nucleus, using this method. The difficulties in identifying the connection between p53 and S100A4 might be explained if the interaction between S100A4 and p53 represents a stage in the biological processes that leads to p53 ubiquitination and destruction. Our findings imply that S100A4 is involved in MDM2-dependent p53 ubiquitination and degradation, given the nucleus localization of the interaction between S100A4 and p53 and the fact that lower S100A4 levels result in enhanced p53 stability.

The findings provided here are particularly significant because p53 is one of the most well-known tumor suppressor proteins. An abundance of evidence suggests

#### *Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

that p53 inactivation is essentially required for tumor growth. S100A4, a protein that is commonly overexpressed in malignancies and has been linked to poor prognosis, may contribute to p53 degradation through its interaction with p53, according to the findings. These findings clearly indicate why high S100A4 expression is advantageous to tumor development, and they also explain why S100A4 has a poor prognostic impact in clinical trials. Taken together, the findings imply that, in addition to raising the risk of metastasis as previously demonstrated, increased S100A4 expression in malignancies has the ability to suppress p53 activity. This research also suggests that S100A4 expression in clinical samples should be investigated in connection to cisplatin sensitivity to see if S100A4 may be used as a predictor of cisplatin therapy response. Also TAGLN mediated regulatory network implicated in proteinuria development was used. These findings add to our understanding of the molecular pathways driving proteinuria etiology. Recent study has uncovered a significant feature of the protein that makes it a promising candidate for further investigation as a therapeutic target: its specific control of activity levels and expression in cancer cell lines. In both epithelial and mesenchymal cells, the Rho family GTPases play an important role in directing the dynamics of the actin cytoskeleton. There is strong evidence that EMT is linked to the production of the vimentin protein, which is phosphorylated and reoriented in cells, regulating cell contraction and focal adhesion assembly and disassembly. During metastasis, there is also crosstalk between distinct components of the cytoskeleton. The use of actin-binding proteins as new therapeutic targets has a lot of promise for the creation of specific cancer medicines, according to researchers also when employing phenotypic screening to get positive results, there are a lot of procedural concerns to keep in mind. In conclusion, in addition to the crucial role of the RLC phosphorylation in driving the myosin IIA's conformations. These novel findings and analyses are attracting a lot of attention because they have the potential to lead to ground-breaking outcomes in our fight against cancer and drug-resistant cancer cells by combining traditional cancer therapy with EMT-related mechanisms. The findings imply that the mix of cytoskeletal components plays a critical role in the modulation of cytoskeletal dynamics in cancer.

*Cancer Bioinformatics*

### **Author details**

Ban Hussein Alwash1 , Rawan Asaad Jaber Al-Rubaye2 , Mustafa Mohammad Alaaraj3 and Anwar Yahya Ebrahim4 \*

1 Faculty of Dentistry, Department of Microbiology, Babylon University, Babylon, Iraq

2 Uclan Medical School, University of Central Lancashire, Uclan, UK


\*Address all correspondence to: anwaralawady@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components DOI: http://dx.doi.org/10.5772/intechopen.101624*

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#### **Chapter 4**

## Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis

*Arpana Parihar, Shivani Malviya and Raju Khan*

#### **Abstract**

Among the leading cause of death cancer ranked in top position. Early diagnosis of cancer holds promise for reduced mortality rate and speedy recovery. The cancer associated molecules being altered in terms of under/over expression when compared to normal cells and thus could act as biomarkers for therapeutic designing and drug repurposing. The information about the known cancer associated biomarkers can be exploited for targeting of cancer specifically in terms of selective personalized medicine designing. This chapter deals with various types of biomarkers associated with different types of cancer and their identification using integrated bioinformatic analysis. Besides, a brief insight on integrated bioinformatics analysis tools and databases have also been discussed.

**Keywords:** Cancer, biomarkers, therapy, computational biology, differentially expressed genes

#### **1. Introduction**

Cancer is the dreadful disease in which cells divide uncontrollably and, at a later stage, begin attacking neighboring tissues. Hereditary mutations, toxin exposure, radiation exposure, alcohol usage, smoking, and radical lifestyle changes are all known to cause cancer. Early detection of cancer results in good therapy. The traditional diagnostic procedures of X-ray, CT-scan, and tissue biopsy are unable to detect it at an early stage, resulting in a delay in treatment that has resulted in the death of several people globally due to cancer [1, 2]. Substantial advances in cancer biology have resulted in the discovery of various biomolecules that are especially linked to cancer progression and development, and therefore referred to as "biomarkers." Biomarkers are basically alterations which are cellular, biochemical, and molecular changes that can be used to identify or monitor a normal, abnormal, or just a biological process. They are utilized to test and evaluate pathogenic processes, normal biological processes, and the pharmacological response to a treatment intervention objectively. Biomarkers could be classified based on their chemical nature and functionality that can be identified using transcriptomics, metabolomics, genomics and proteomics (**Figure 1**) [3, 4].

#### **Figure 1.**

*Analysis of potential biomarkers using different integrated bioinformatics analysis assay platforms such as DNA based from FISH assay platform, RNA based biomarkers from micro arrays, protein based biomarkers from proteomic profiles and metabolites based on biomarkers from metabolomics profiles which led to screening of various kinds of cancer resulting in identification of potential candidate genes for prognostic therapeutic approach.*

Usually, living cells have a finite life span, and their genome deoxy ribonucleic acid (DNA) transcribes into ribonucleic acid (RNA), which upon translation results in the creation of proteins that participate in numerous physiological and metabolic processes required by the body. Any change in these mechanisms, such as a mutation in DNA, causes disruption which leads to a dreadful disease namely, Cancer. The detection of mutations in DNA can be used to predict Cancer risk [5]. Consequently, measurement of RNA, protein, and metabolite expression levels can provide important information about illness progression and profiling. There are more than 200 types of cancer reported, however in this chapter, we gathered and presented information about various biomarkers associated with top 5 types of cancer in the world, which can be exploited in designing of sensitive and effective diagnostic technology for early detection of cancer. Basically, various types of biomarkers associated with different types of cancer and their identification using integrated bioinformatic analysis will be discussed. Besides, a brief insight on integrated bioinformatics analysis tools and databases have also been discussed.

#### **2. Biomarkers associated with different types of cancer**

Biomarkers have been generally known to play crucial role in the association with different cancer resulting in therapeutic aspects. These could be constructed with the help of advanced integrated bioinformatics analysis tools which could provide an ease to identify biomarkers which could be treated as potential candidates to treat diversities of Cancer. We have listed biomarkers associated with various types of cancer using integrated bioinformatics approaches in **Table 1**. The mechanistic insight regarding how the databases can be utilized to extract and identify various biomarkers associated with respective cancers have been depicted in **Figure 2**.


*Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

#### **Table 1.**

*Biomarkers identified by using integrated bioinformatics tools, associated with various types of cancer such as lung cancer, gastric cancer, colorectal cancer.*

#### **Figure 2.**

*The schematic representation of extraction of datasets from the GEO database then the identification of DEGs followed by its functional analysis and subsequent qPCR validation leading to identification of small molecule known as biomarker for treating Cancer.*

#### **2.1 Lung cancer**

Lung cancer is the most common cancer-related death around the globe. Despite great attempts to enhance treatment approaches in previous decades, the clinical outcome of traditional therapies such as surgery, radiation, and chemotherapy remains poor when compared to other major forms of cancer such as colon, prostate, and breast cancers. The challenges in making an early-stage diagnosis of lung cancer and the high recurrence rate after curative treatments are the main reasons for the lack of improvement in prognosis [28]. To improve the clinical result of lung cancer treatments, it is critical to identify and validate diagnostic and prognostic biomarkers. Therefore, here in this section of chapter we have reviewed studies led by certain researchers for identification of the lung cancer biomarkers using integrated bioinformatics analysis. There are mainly 2 types of the lung cancer. In 80–85% cases, the type of lung cancer is non-small cell lung cancer (NSCLC). The main subtypes of which are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. These subtypes generally begin from different types of the lung cells that are grouped together as NSCLC and their treatment and prognoses are almost similar. The other type is small cell lung cancer (SCLC) and around 10–15% of all lung cancers are SCLC and it is sometimes called oat cell cancer. SCLC grows and spread faster than NSCLC.

In a study by Ni et al., four GEO datasets GSE18842, GSE19804, GSE43458, and GSE62113, were extracted form Gene Expression Omnibus (GEO) database into which the limma package was used to assess differentially expressed genes (DEGs) between NSCLC and normal samples, and the RobustRankAggreg (RRA) programme was used to undertake gene integration. Furthermore, they established the protein–protein interaction (PPI) network of these DEGs using the Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE). Funrich (http://www.funrich.org)and OmicShare (https://www.omicshare.com/tools/)were also conducted to ensure functional

#### *Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

enrichment and pathway enrichment analysis for DEGs. Besides this, they used the gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online datasets to analyze the expressions and prognostic values of top genes. Hence, it led to the identification of a total of 249 DEGs including 113 upregulated and 136 downregulated after gene integration. Followed by this, they established a PPI network with 166 nodes and 1784 protein pairings resulting in TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 to be considered as possible important genes, whereas they further added, the mitotic cell cycle pathway to play a crucial role in NSCLC advancement resulting in its employment as a novel biomarker for NSCLC diagnosis and to guide synthesis medication [6].

In another study by Dai et al., 6 key biomarkers associated with non- small cell lung cancer in which GEO2R were analyzed to examine three microarray datasets from the Gene Expression Omnibus collection along with the enrichment analysis which was performed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Further, the String database, Cytoscape, and the MCODE plug-in were then used to build a PPI network and screen hub genes using the String database, Cytoscape, and the MCODE plug-in. Kaplan–Meier curves were used to examine overall and disease-free survival of hub genes, as well as the association between target gene expression patterns and tumor grades. To verify enrichment pathways and diagnostic effectiveness of hub genes, researchers performed gene set enrichment analysis and receiver operating characteristic curves. A total of 293 differentially expressed genes were discovered, with cell cycle, ECM–receptor interaction, and malaria being the most prevalent. The PPI network identified 36 hub genes, six of which were reported to have important roles in NSCLC (non- small cell lung cancer) carcinogenesis: CDC20, ECT2, KIF20A, MKI67, TPX2, and TYMS. The target genes discovered can be employed as potential biomarkers to identify and diagnose nonsmall cell lung cancer as per their investigations [7].

Similarly, in another study by Cui et al., they used integrated bioinformatic analysis of multivariate large-scale databases to assess the potential of DEAD/H box helicases as prognostic indicators and therapeutic targets in lung cancer. They were able to discover four biomarkers with the most significant changes after analyzing the survival and differential expression of these helicases. The unfavorable prognostic factors DDX11, DDX55, and DDX56, as well as the good prognosis factor DDX5, were discovered. MYC signaling is adversely linked with DDX5 gene expression, but favorably associated with DDX11, DDX55, and DDX56 gene expression, according to pathway enrichment analysis led by them. Low mutation levels of TP53 and MUC16, the two most frequently mutated genes in lung cancer, are related with high expression levels of the DDX5 gene. High levels of DDX11, DDX55, and DDX56 gene expression, on the other hand, were linked to high levels of TP53 and MUC16 mutation. The levels of DDX5 gene expression in tumor-infiltrated CD8 + T and B cells are positively correlated, but the other three DEAD box helicases are negatively correlated. Furthermore, while each DDX has a unique miRNA signature, the DDX5-associated miRNA profile is distinct from the miRNA profiles of DDX11, DDX55, and DDX56. The discovery of these four DDX helicases as biomarkers could be considered useful for lung cancer prognostication and targeted treatment development [8].

In another study by Liao et al., they have identified candidate genes associated with the pathogenesis of small cell lung cancer analyzed using integrated bioinformatics tools. GSE60052, GSE43346, GSE15240, and GSE6044 were the four datasets that they downloaded from the Gene Expression Omnibus. R software was used to examine the differentially expressed genes (DEGs) between the SCLC and normal

samples. For each dataset, the limma software was utilized. The DEGs from the four datasets were combined using the RobustRankAggreg package. FunRich software and R software were used to conduct functional and route enrichment analyses using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, accordingly. The DEGs' protein–protein interaction (PPI) network was also built using the STRING database and the Cytoscape software. Molecular Complex Detection in Cytoscape software was used to find hub genes and important modules. Ultimately, the Oncomine online database was used to assess the expression values of hub genes. Following the integration of the four datasets, 412 DEGs were discovered, comprising 146 upregulated genes and 266 downregulated genes. The increased DEGs were mostly involved in cell division, cell cycle, and microtubule binding. The complement and coagulation cascades, the cytokine-mediated signaling pathway, and protein binding were all heavily represented among the downregulated DEGs. Based on a subset of the PPI network, eight hub genes and one major module connected to the cell cycle pathway were discovered. Eventually, in comparison to normal tissue, five hub genes were shown to be substantially expressed in SCLC tissue. The cell cycle route may be the one that is most closely linked to SCLC pathophysiology. As a result, follow-up studies in the diagnosis and therapy of SCLC should focus on NDC80, BUB1B, PLK1, CDC20, and MAD2L1 [9].

In another similar study by Tu et al., GEO2R was used to search the mRNA microarray datasets GSE19188, GSE33532, and GSE44077 for differentially expressed genes (DEGs). The DEGs were analyzed for functional and pathway enrichment using the DAVID database. STRING was used to create a protein–protein interaction (PPI) network, which was then displayed in Cytoscape. MCODE was used to analyze the PPI network's modules. The Kaplan Meier-plotter was used to analyze the overall survival (OS) of genes from MCODE. Total of 221 DEGs were found, with words linked to cell division, cell proliferation, and signal transduction being the most abundant. A PPI network with 221 nodes and 739 edges was created. The PPI network revealed a substantial module containing 27 genes. UBE2T, UNF2, CDKN3, ANLN, CCNB2, and CKAP2L all have high expression levels and have been linked to a poor prognosis in NSCLC patients. Protein binding, ATP binding, cell cycle, and the p53 signaling pathway were among the enriched functions and pathways. DEGs in non- small cell lung cancer (NSCLC) have the potential to be useful targets for diagnosing and treating the disease [10].

In another study by Liu et al., in this prospective investigation, which included 46 tumors and 45 controls, the gene expression profile GSE18842 was acquired from the Gene Expression Omnibus database. They used functional enrichment analysis and KEGG analysis using upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively, after screening differentially expressed genes (DEGs). The STRING database was used to create protein–protein interaction (PPI) networks between DEGs and their corresponding coding protein complexes, which were then examined using Cytoscape. The Kaplan– Meier approach was used to confirm the survival of hub genes. In the TCGA database, the gene expression level heat map of hub genes between NSCLC and neighboring lung tissues was plotted using the GEPIA webserver. After gene integration, they found 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples compared to control samples. They built a PPI network for the DEGs with 249 nodes and 1472 protein pairings on the edges. Survival study confirmed that ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were related with lower overall survival in

*Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

NSCLC. The GEPIA web tool was used to verify the expression dependability of hub genes. The findings suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent critical biomarkers for diagnosis and prognosis, and that the mitotic cell cycle pathway is a likely signaling pathway contributing to NSCLC progression, according to KEGG analysis. Such genes could be useful diagnostic biomarkers, as well as a new strategy to designing targeted NSCLC treatments [11].

#### **2.2 Gastric cancer**

Despite a substantial drop in incidence and death in North America and most Western European countries in recent decades, gastric cancer (GC) remains the fifth most prevalent malignancy worldwide and poses a serious medical burden, particularly in Eastern Asia [29, 30]. The fact that most patients are discovered at an advanced stage, even with metastatic illnesses, and thus miss out on the potential for a curative resection, accounts for the poor 5-year survival in GC [31, 32]. Substantial progress has been made in comprehending the epidemiology, pathophysiology, and molecular mechanisms of GC, as well as in implementing new therapy alternatives like as targeted and immune-based therapies, not all patients react to molecularly targeted medications developed for specific biomarkers [32, 33]. Hence, due to molecular complexity, poor prognosis, and significant reoccurrence of GC, new diagnostic and prognostic biomarkers are urgently needed [34, 35]. Microarray and high-throughput sequencing technologies have advanced in recent years, allowing researchers to decipher important genetic or epigenetic changes in carcinogenesis and discover promising biomarkers for cancer diagnosis, treatment, and prognosis [36]. Nevertheless, integrated bioinformatics methods have been used in cancer research to overcome limited or inconsistent results due to the use of different technology platforms or a small sample size, and a large range of valuable biological information has been revealed [37–39].

Hence, here we have reviewed a few studies to ensure the role of biomarker identification associated to gastric cancer using integrated bioinformatics analysis tools. In a study by Liu et al., they have considered TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1 which are nine hub genes that may be linked to the etiology of GC. Hence, CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 were used to construct a prognostic gene signature that performed well in predicting overall survival. An integrated analysis of several gene expression profile datasets was used by them to find differentially expressed genes between GC and normal gastric tissue samples. Furthermore, protein–protein interaction network and Cox proportional hazards model studies were used to identify key genes related to the pathophysiology and prognosis of GC resulting in their constructed gene signature to be considered as a potential candidate for the biomarker to facilitate the molecular targeting therapy of GC [12].

In a study by Wang et al., they discovered promising biomarkers that could be used to diagnose GC patients. Four Gene Expression Omnibus (GEO) datasets were obtained and examined for differentially expressed genes to look for possible treatment targets for GC (DEGs). The function and pathway enrichment of the discovered DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A network of protein–protein interactions (PPI) was created. The degree of connection of proteins in the PPI network was calculated using the CytoHubba plugin of Cytoscape, and the two genes with the highest degree

of connectivity were chosen for further investigation. The two DEGs with the highest and lowest log Fold Change values were also chosen. Oncomine and the KaplanMeier plotter platform were used to investigate these six important genes further. A total of 99 genes that were upregulated and 172 genes that were downregulated across all four GEO datasets were examined. The Biological Process phrases 'extracellular matrix organization,' 'collagen catabolic process,' and 'cell adhesion' were primarily enriched in the DEGs. The categories 'ECMreceptor interaction,' 'protein digestion and absorption,' and 'focal adhesion' were considerably enriched in these three KEGG pathways. According to Oncomine, ATP4A and ATP4B expression were downregulated in GC, while all other genes were increased. Upregulated expression of the identified important genes was substantially associated with worse overall survival of GC patients, according to the KaplanMeier plotter platform. The current findings imply that FN1, COL1A1, INHBA, and CST1 could be used as gastric cancer biomarkers and treatment targets. Additional research is needed to determine the role of ATP4A and ATP4B in the treatment of gastric cancer [13].

In another study by Rong et al., their research outlines an integrated bioinformatics approach to identifying molecular biomarkers for stomach cancer in cancer tissues of patients. In large gastric cancer cohorts, they reported distinct expression genes from Gene Expression Ominus (GEO). Their findings found that 433 genes in human stomach cancer have significantly distinct expression patterns. Bioinformatic studies and co-expression network design were used to confirm the different gene expression profiles in gastric cancer. They identified collagen type I alpha 2 (COL1A2), which encodes the pro-alpha2 chain of type I collagen whose triple helix comprises two alpha1 chains and one alpha2 chain, as the key gene in a 37-gene network that modulates cell motility by interacting with the cytoskeleton, based on the co-expression network and top-ranked genes. Immunohistochemistry on human gastric cancer tissue was also used to investigate the predictive function of COL1A2. When compared to normal gastric tissues, COL1A2 was substantially expressed in human gastric cancer. The level of COL1A2 expression was found to be substantially related to histological type and lymph node status after statistical analysis. There were no links found between COL1A2 expression and age, lymph node count, tumor size, or clinical stage. Finally, the unique bioinformatics used in this study led to the discovery of improved diagnostic biomarkers for human stomach cancer, which could aid future research into the crucial change that occurs during the disease's course [14].

In another study, the goal of their research is to find an lncRNA-related signature that can be used to assess the overall survival of 379 GC patients from The Cancer Genome Atlas (TCGA) database. The univariate and multivariate Cox proportional hazards regression models were used to assess the correlations between survival outcome and the expression of lncRNAs. Overall survival was found to be substantially linked with four lncRNAs (LINC01018, LOC553137, MIR4435-2HG, and TTTY14). These four lncRNAs were combined to form a prognostic signature. There was a strong favorable link between overall survival and GC patients with low-risk scores (P = 0.001). Subsequent research found that the predictive usefulness of this fourlncRNA pattern was unaffected by clinical characteristics. These four lncRNAs were linked to many tumor molecular pathways, according to gene set enrichment analysis. Based on bioinformatics analysis, their research suggests that this unique lncRNA expression pattern could be a helpful diagnostic of prognosis for GC patients [15].

The researchers wanted to see if there were any long noncoding RNAs (lncRNAs) that were linked to the pathophysiology and prognosis of GC. The Gene Expression Omnibus (GEO) database was used to retrieve raw noncoding RNA microarray

*Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

data (GSE53137, GSE70880, and GSE99417). After gene reannotation and batch normalization, an integrated analysis of various gene expression profiles was used to screen for differentially expressed genes between GC and neighboring normal stomach tissue samples. The Cancer Genome Atlas (TCGA) database validated the presence of differentially expressed genes. To identify hub lncRNAs and explore possible biomarkers related to GC diagnosis and prognosis, researchers used a competitive endogenous RNA (ceRNA) network, Gene Ontology term, and Kyoto Encyclopedia of Genes and Genomes pathway, as well as survival analysis. After intersections of differential genes between the GEO and TCGA databases, a total of 246 integrated differential genes were identified, including 15 lncRNAs and 241 messenger RNAs (mRNAs). Three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs), and 72 mRNAs make up the ceRNA network. Three lncRNAs controlled the cell cycle and cellular senescence, according to functional analyses. The survival rate of HMGA1P4 was statistically connected to the total survival rate, according to a survival analysis. They discovered that HMGA1P4, a miR-301b/miR-508 target, regulates CCNA2 in the GC and is implicated in cell cycle and senescence. Ultimately, three lncRNAs' expression levels were shown to be elevated in GC tissues. As a result, three lncRNAs, UCA1, HOTTIP, and HMGA1P4, may play a role in GC development, and their possible functions may be linked to GC prognosis [16].

#### **2.3 Liver cancer**

Liver cancer is among the most frequent malignancies in the world, and it is the second largest cause of cancer death [40, 41]. Due to advances in detection and therapy, people with liver cancer still have a terrible prognosis. Most patients are already in severe stages of symptoms and miss the opportunity to undertake radical resection due to the lack of distinct clinical signs in the early stages. As a result, understanding the pathophysiology of liver cancer aids in early detection, treatment selection, scheduling of follow-up appointments, and prognosis evaluation, all of which can help patients with liver cancer live longer [42]. MicroRNAs (miRNAs) are improperly expressed in a range of tumors and are linked to the pathogenesis of cancers, including liver cancer, according to growing evidence. As tumor suppressor genes or oncogenes, miRNAs play a role in the development of liver cancer. As a result, more research into miRNA expression patterns and consequences could lead to the discovery of new diagnostic or therapeutic targets for liver cancer. Hence, here in this subsection of this chapter we have reviewed certain researches which provide a potential aspect toward identification of biomarkers associated with cancer in relevance to liver utilizing integrated bioinformatics analysis.

Hepatitis B virus (HBV) infection has long been known as a major risk factor for hepatocellular carcinoma (HCC), accounting for at least half of all HCC cases worldwide. Yet, the underlying molecular mechanism of HBV-associated HCC is still unknown. Hence, in an investigation led by Ji et al., they retrieved three microarray datasets from the Gene Expression Omnibus (GEO) collection, including 170 tumoral samples and 181 adjacent normal tissues from the liver of patients with HBV-related HCC which were subjected to integrated analysis of differentially expressed genes (DEGs). Following that, the protein–protein interaction network (PPI) and function and pathway enrichment analyses were carried out. The expression profiles and survival analyses of the ten hub genes selected from the PPI network were carried out. Overall, 329 DEGs were discovered in which 67 were upregulated and 262 were downregulated. PDZ-binding kinase (PBK), abnormal spindle microtubule

assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F were among the ten DEGs with the highest degree of connectivity (CENPF). Overexpression levels of KIF2C and TPX2 were linked to both poor overall survival and relapse-free survival in a Kaplan–Meier study. Therefore, the hub genes identified in this investigation could be useful in the diagnosis, prognosis, and treatment of HBV-related HCC. Furthermore, their research identifies a number of important biological components (e.g., extracellular exosomes) and signaling pathways that are involved in the progression of HCC caused by HBV, providing a more thorough understanding of the mechanisms underlying HBV-related HCC [17].

In another study by Shen et al., they created nine co-expression modules and discovered that in liver cancer, miR1055p, miR7675p, miR12665p, miR47465p, miR500a3p, miR11803p, and miR1395p were differentially expressed. These miR-NAs were found to have a strong link to the prognosis of patients with liver cancer. MiR1055p and miR1395p may be considered separate prognostic variables among them. As a result, seven miRNAs could be used as predictive indicators in the case of liver cancer [18].

In another study by Li et al., The GSE19665, GSE33006, and GSE41804 microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found and function enrichment analyses were carried out. STRING and Cytoscape were used to create the protein–protein interaction network (PPI) and perform module analysis. There were a total of 273 DEGs found, with 189 downregulated genes and 84 upregulated genes. Protein activation, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle, and oocyte meiosis are among the DEGs' enhanced activities and pathways. A biological process study found that these genes were primarily abundant in cell division, cell cycle, and nuclear division. BUB1, CDC20, KIF20A, RACGAP1 and CEP55 were found to be involved in the carcinogenesis, invasion, and recurrence of HCC in a survival analysis. Finally, the DEGs and hub genes discovered in this work contribute to our understanding of the molecular pathways underlying HCC carcinogenesis and development, as well as providing candidate targets for HCC diagnosis and treatment [19].

#### **2.4 Breast cancer**

Breast cancer is becoming more common over the world, and it is now considered a serious disease among women. Asia has recently emerged as a high-risk location for breast cancer, ranking first among female malignant tumors [43, 44]. Breast cancer therapy has improved recently as a result of constant efforts and advances in contemporary medicine, and the death rate of breast cancer has decreased dramatically. Recurrence and metastasis of breast cancer, on the other hand, have remained unaddressed and have become the most difficult clinical difficulties [43, 45]. To better understand the functions of tumor-related genes and the roles of tumor cell signaling pathways, researchers are turning to genetic studies. Together bioinformatics and system biology are strong multidisciplinary topics that combine biological information collecting, storage, processing, and distribution, summarize life sciences and computer science, and collect and analyze genetic data [46, 47]. Hence, here in this chapter we have reviewed a few studies led by researchers to identify most prevalent biomarkers associated with breast cancer utilizing integrated bioinformatics approaches.

#### *Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

In an investigation by Wang et al. they have analyzed gene expression profiles of GSE48213 using Gene Expression Omnibus database. Further, validation was done using RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas. In their study, they identified the gene co- expression network which revealed four modules, one of which was found to be strongly linked with patient survival time. They found that the black module which was found to be basal, was made up of 28 genes; the dark red module which was found to be claudin-low, was made up of 18 genes; the brown module which was found to be luminal, was made up of nine genes; and the midnight blue module was made up of seven genes which was investigated to be nonmalignant. Due to a considerable difference in survival time between the two groups, these modules were clustered into two groups. Hence, TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module were identified by them as the genes with the highest betweenness, implying that they play a central role in information transfer in the network. Therefore, TXN, ANXA2, TPM4, LOXL2, TPRN, ADCY6, TUBA1C, and CMIP are eight hub genes that have been identified and validated by them as being linked to breast cancer progression and poor prognosis to be considered [20].

In another study by Wu et al., Differentially expressed genes (DEGs) in breast cancer were discovered using three data sets from the GEO database. The functional roles of the DEGs were determined using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes pathway studies. They also used the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases to look at the translational and protein expression levels, as well as survival statistics, of DEGs in patients with breast cancer. Using miRWalk and TargetScan, the corresponding change in the expression level of microRNAs in DEGs was predicted, and the expression profiles were evaluated using OncomiR. Finally, RT-qPCR was used to confirm the expression of new DEGs in Chinese breast cancer tissues. ADH1A, IGSF10, and the 14 microRNAs have all been identified as promising new biomarkers for breast cancer diagnosis, therapy, and prognosis [21].

In another study by Cai et al., the Gene Expression Omnibus (GEO) database was used to obtain GSE102484 gene expression profiles. The most potent gene modules related with the metastatic risk of breast cancer were found using weighted gene co-expression network analysis (WGCNA), which yielded a total of 12 modules. 21 network hub genes (MM > 0.90) were kept for further analysis in the most significant module (R2 = 0.68). The biomarkers with the greatest interactions in gene modules were then investigated further using protein–protein interaction (PPI) networks. Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as important genes associated with breast cancer progression by the PPI networks. Furthermore, using data from The Cancer Genome Atlas (TCGA) and the Kaplan–Meier (KM) Plotter, the predictive value and differential expression of these genes were confirmed. The mRNA expression levels of these five hub genes have excellent diagnostic value for breast cancer and surrounding tissues, according to a Receiver Operating Characteristic (ROC) curve study. Furthermore, KM Plotter revealed that these five hub genes were substantially related with lower distant metastasis-free survival (DMFS) in the patient group. Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) linked to the likelihood of distant metastasis were extracted for future study and could be employed as biomarkers to predict breast cancer distant metastasis [22].

In another study by Wu et al., there were a total of 215 DEGs found, with 105 upregulated genes and 110 downregulated genes. The enriched keywords and pathways were primarily linked to cell cycle, proliferation, drug metabolism, and oncogenesis, according to GO and KEGG analyses. Cell Division Cycle 45 (CDC45), Polo Like Kinase 1 (PLK1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cell Division Cycle 20 (CDC20), Aurora Kinase A (AURKA), and Mitotic Arrest Deficient 2 Like 1 were identified as hub genes from the PPI network (MAD2L1). These hub genes' resilience was confirmed by survival analysis and expression validation tests [23].

#### **2.5 Colorectal cancer**

CRC (colorectal cancer) is one of the top causes of death among cancer patients around the world. Older age, male sex, lifestyle, inflammatory bowel illness, and a previous personal history of CRC are all risk factors for the disease. A positive family history is also substantially linked to a higher lifetime relative risk of CRC diagnosis. CRC, on the other hand, is an indolent disease in its early stages, becoming symptomatic only when it evolves to more advanced stages. Numerous attempts have been made to develop adequate screening technologies, but they remain intrusive even now, resulting in reduced attainment rates among large community [48]. Recent breakthroughs in our understanding of the molecular underpinnings and cellular mechanisms of CRC have resulted in the widespread use of particular molecular diagnostics in clinical practice. The patient's risk is stratified and therapy is decided based on the test results. Conversely, current research into biomarkers associated with colorectal cancer could usher in a new age in diagnosis, risk prediction, and treatment selection. Here, we have reviewed a few investigations led to ensure its attainment using integrated bioinformatics analysis [49].

In an investigation led by Chen et al., they analyzed 207 common DEGs in colorectal cancer using the integrated GEO and TCGA databases into which they constructed a PPI network consists of 70 nodes and 170 edges and identified 10 top hub genes. A prognostic gene signature which includes SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was constructed by them which revealed overall survival in patients suffering from CRC. Hence, it could be considered as a good potential candidate for further treatments [24].

In a study by Dai et al., they discovered nine differentially expressed lncRNAs and their putative mRNA targets using integrated data mining. They evaluated key pathways and GO words that are associated to the up-regulated and down-regulated transcripts, respectively, after a series of bioinformatics investigations. Meanwhile, qRT-PCR was used to validate the nine lncRNAs in 30 matched tissues and cell lines, and the results were largely compatible with the microarray data. They also looked for nine lncRNAs in the blood of 30 CRC patients with tissue matching, 30 noncancer patients, and 30 healthy people. Finally, they discovered that BLACAT1 was important for CRC diagnosis. Between CRC patients and healthy controls, the area under the curve (AUC), sensitivity, and specificity were 0.858 (95% CI: 0.765–0.951), 83.3%, and 76.7%, respectively. Furthermore, BLACAT1 exhibited a particular utility in distinguishing CRC from non-cancer disorders. The findings suggest that significantly elevated lncRNAs as well as associated potential target transcripts could be used as therapeutic targets in CRC patients. Conversely, the lncRNA BLACAT1 could be a new supplemental biomarker for CRC detection [25].

In another study by Sun et al., The Gene Expression Omnibus (GEO) mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were collected *Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

and processed with bioinformatics to discover hub genes in CRC development. The GEO2R tool was used to look for differentially expressed genes (DEGs). The DAVID database was used to conduct gene ontology (GO) and KEGG studies. To build a protein–protein interaction (PPI) network and identify essential modules and hub genes, researchers employed the STRING database and Cytoscape software. The DEGs' survival studies were done using the GEPIA database. Potential medications were screened using the Connectivity Map database. There were a total of 865 DEGs found, with 374 upregulated and 491 downregulated genes. These DEGs were mostly linked to metabolic pathways, cancer pathways, cell cycle pathways, and so on. With 863 nodes and 5817 edges, the PPI network was discovered. HMMR, PAICS, ETFDH, and SCG2 were found to be strongly linked with overall survival of CRC patients in a survival analysis. Blebbistatin and sulconazole have also been discovered as potential treatments [26].

Falzone et al. used the mirDIP gene target analysis in a sample of 19 differentially expressed miRNAs to determine the interaction between miRNAs and the most changed genes in CRC. DIANA-mirPath prediction analysis was used to identify miRNAs that can activate or inhibit genes and pathways involved in colorectal cancer development. As a whole, these studies found that the up-regulated hsa-miR-183-5p and hsa-miR-21-5p, as well as the down-regulated hsa-miR-195-5p and hsa-miR-497-5p, were linked to colorectal cancer development via interactions with the Mismatch Repair pathway and the Wnt, RAS, MAPK, PI3K, TGF-, and p53 signaling pathways [27].

#### **3. Integrated bioinformatics analysis tools and databases**

Various integrated bioinformatics databases have been utilized for the identification of prognostic biomarkers in the treatment of various kinds of cancer. Some of which have been enlisted in **Table 2** along with database links. The biomarkers associated with different types of Cancers identified with the help of integrated bioinformatics tools depicted in **Figure 3**.

#### **3.1 Microarray and RNASeq data collection**

The microarray data collection is done using the GEO database which refers to Gene Expression Omnibus. It could be easily accessed via online medium using http://www.ncbi.nlm.nih.gov/geo/link. The GEO database is basically being used to obtain high-throughput gene expression profiles of PTC (Papillary thyroid carcinoma) and normal thyroid tissues. Independent datasets are chosen, and they are all based on the specified platforms, including the relevant tissues. As per our review of various studies which are aforementioned in this chapter, various microarray datasets have been collected using the GEO database and then processed with bioinformatics to discover hub genes. Several new technologies have emerged for the analysis of gene expression and for the identification of cancer biomarkers. One such technology is RNASeq technology which is nowadays considered to be the most up to date technology to analyze gene expression. Into this technology, with the use of NGS (Next generation genome sequencer) the gene expression profile analysis carried out. The first stage in the process is to convert the population of RNA to be sequenced into complementary DNA (cDNA) fragments which is present in biological sample (a cDNA library). This is accomplished using reverse transcription, allowing the RNA


#### **Table 2.** *List of databases used for data mining.*

#### **Figure 3.**

*Mechanistic insight of extraction, construction and identification of biomarkers associated with different kinds of cancers with the help of integrated bioinformatics tools.*

*Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

to be used in an NGS procedure. After that, the cDNA is fragmented, and adapters are attached to each fragment's end. The functional elements present on adopters which allowed sequencing. The cDNA library is evaluated by NGS after amplification, size selection, clean-up, and quality verification, yielding short sequences that correspond to all or part of the fragment from which it was formed. The extent to which the library is sequenced is determined by the intended use of the output data. Sequencing can be done in one of two ways: single-end or paired-end. Single-read sequencing is a less expensive and faster method of sequencing cDNA fragments from only one end (approximately 1% of the cost of Sanger sequencing). While paired-end approaches are more expensive since they sequence from both ends, but they provide advantages in post-sequencing data reconstruction. After completing the RNA sequencing technology workflow, the data can be matched to a reference genome if one is available, or built from scratch to provide an RNA sequence map that encompasses the transcriptome. A bioinformatics workflow is developed to discover various alternative biomarkers via LC- MS/MS technique (liquid chromatography coupled tandem mass spectrometry). Further, open Mass spectrometry Search Algorithm is used against the customized alternative splicing database along with the preferred cancer plasma proteome for the identification of respective biomarker [50, 51].

#### **3.2 Screening of DEGs**

The GEO2R program which could be easily accessed via http://www.ncbi.nlm.nih. gov/geo/geo2r/link, is used for the detection of these differentially expressed genes which are known as DEGs. Further, R package Limma is been utilized to screen out these DEGs.

#### **3.3 Enrichment analysis via GO and KEGG pathway**

Followed by the screening of DEGs, the enrichment analysis using GO and KEGG pathway is performed using the database for Annotation, Visualization and Integrated Discovery, commonly known as DAVID database (http://david.abcc.ncifcrf.gov/). This process includes biological processes, cellular components, molecular function and KEGG pathway analysis. Further, the GOplot package of R could be used to display the results of analysis and the pathway analysis results can also be analyzed using the clueGO plug-ins of cytoscape software 3.7.2. [52].

#### **3.4 Construction of the PPI network and analysis of the module**

After the enrichment analysis, the PPI network is being built upon using the STRING (http://www.bork.embl-heidelberg.de/STRING/) database which refers to Search Tool for the Retrieval of Interacting Genes/Proteins, to uncover DEG associations based on minimum prescribed interaction scores. Followed by this, using the Cytoscape (http://www.cytoscape.org/) database, the PPI network is then analyzed and visualized. Additionally, MCODE is also one such bioinformatics tool utilized to screen the PPI network's main module.

#### **3.5 Survival analysis and validation of hub gene expression**

At last. The Cancer Genome Atlas (https://tcga-data.nci.nih.gov/tcga/), was utilized to examine the association between important gene expression and survival of patients with PTC (Papillary thyroid carcinoma). RNA expression data from hundreds of samples from the TCGA and GTEx projects was analyzed using the Gene Expression Profiling Interactive Analysis tool (GEPIA) (http://gepia2021.cancer-pku. cn/). Additionally Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases could also be used to look at the translational and protein expression levels, as well as survival statistics, of DEGs. Apart from this, miRWalk and TargetScan, were used to predict the corresponding change in the expression level of microRNAs in DEGs and the expression profiles were evaluated using OncomiR. Finally, RT-qPCR has been used to confirm the expression of new DEGs. Hence, the constructed biomarkers could be treated as potential candidates for various kinds of Cancers.

#### **4. Challenges and future outlook**

The development of biomarkers for early detection cancer screening and therapy monitoring has biological as well as financial hurdles. The majority of existing cancer detection tools only detect late stage or fully grown cancer, not premalignant or early abnormalities that can be resected and treated. Despite the fact that a screening test may detect cancer just at preclinical stage, it is not suitable for follow-up, and hence may miss micro metastases, limiting the benefits of early identification and treatment [53]. Additional barrier to the development of cancer biomarkers is the fact that cancer is a diverse illness, with several biologically distinct phenotypes that respond differently to treatments. Between cells of a single macroscopic tumor, the nature of its heterogeneity can be found. Biomarker development may be hampered by this variability. As a result, developing biomarkers using genomic and proteomic methods could help to solve the variability challenges [3]. An even more issue is that pre-neoplastic lesions are far more common than aggressive malignancies in several organs, such as the prostate and colon [54]. This addresses the possibility of whether any screening strategy should focus solely on early lesions or should additionally consider the tumor's behavior. In the last two decades, detailed and comprehensive knowledge of cancer at the cellular and molecular levels has increased dramatically and exponentially, resulting in significant improvements in the characterization of human tumors, which has catalyzed a shift toward the development of targeted therapies, the foundation of molecular diagnostics [55, 56]. Omics technology may serve as the foundation for the development of novel cancer biomarker and/or panels that have significant advantages over currently utilized biomarkers. Omics has enhanced the number of potential biomarkers such as DNA, RNA, and other protein biomolecules that may be studied. The previous idea of single biomarker discovery has lately been supplanted by multibiomarker discovery of a panel of genes or proteins, raising the question of whether heterogeneous and complex cancers can have a single fingerprint.

Biomarkers in association with cancer are used in oncology and clinical practice for risk assessment, screening, and diagnosis in combination with other diagnostic methods, and most importantly for determining prognosis and treatment response and/or recurrence. Cancer biomarkers can also help with cancer diagnosis at the molecular level. Clinicians and researchers must have a thorough understanding of the molecular aspects, clinical utility, and reliability of biomarkers in order to determine whether or not a biomarker is clinically useful for patient care and whether or not additional evaluation is required before integration into routine care. Biomarkers, through simplifying the integration of therapies and diagnostics, have the potential to play a key role in the development of customized medicine.

*Identification of Biomarkers Associated with Cancer Using Integrated Bioinformatic Analysis DOI: http://dx.doi.org/10.5772/intechopen.101432*

#### **5. Conclusions**

Research in the field of cancer-specific biomarkers have provided a promising source of novel diagnostic tools. Various groups have reported that altered cancerassociated biomarkers can be exploited to diagnose and monitor various cancers with greater sensitivity and specificity. Assessment of genomic and transcriptomic biomarkers found to be potentially very sensitive approaches for discriminating between cancerous non-cancerous (benign) conditions. Besides, this one could detect cancers at a much earlier stage by quantitative analysis of potential biomarker associated with specific cancer. Given the possible diagnostic power of genomic, transcriptomic, proteomic, and metabolomic biomarkers, these are currently one of the most promising areas of research in the field of development of cancer prognostic and diagnostics devices.

#### **Author details**

Arpana Parihar1 \*, Shivani Malviya<sup>2</sup> and Raju Khan1,3

1 Microfluidics and MEMS Centre, CSIR-Advanced Materials and Processes Research Institute (AMPRI), Bhopal, India

2 Department of Biochemistry and Genetics, Barkatullah University, Bhopal, Madhya Pradesh, India

3 Academy of Scientific and Innovative Research (AcSIR), CSIR-AMPRI, Bhopal, India

\*Address all correspondence to: arpana\_parihar@yahoo.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 5**

## The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions

*Donovan McGrowder, Lennox Anderson-Jackson, Lowell Dilworth, Shada Mohansingh, Melisa Anderson Cross, Sophia Bryan, Fabian Miller, Cameil Wilson-Clarke, Chukwuemeka Nwokocha, Ruby Alexander-Lindo and Shelly McFarlane*

#### **Abstract**

Worldwide, prostate cancer (PCa) is the leading cause of morbidity and cancer-related mortality in men. The pathogenesis of PCa is complex and involves abnormal genetic changes, abrogation of cell growth with heterogeneous progression and predictive subgroups. In the last two decades there have been the exploration and development of molecular and genetic biomarkers for PCa due to limitations of traditional serum biomarkers such as prostate specific antigen (PSA) in screening and diagnosis. These biomarkers could possibly differentiate between PCa and benign prostatic hyperplasia (BPH) patients, and healthy controls as well as assist with prognosis, risk stratification and clinical decision-making. Such molecular biomarkers include serum (PHI and 4K score), urine (PCA3 and SelectMDx), and tumor tissue (Oncoytype DX, Decipher and Prolarix). microRNAs (miRNAs) deregulation where there is increased or decreased expression levels, constitute prospective noninvasive molecular biomarkers for the diagnosis and prognosis of PCa. There are also other emerging molecular biomarkers such as exosomal miRNAs and proteins that are in various stages of development and clinical research. This review is intended to provide a wide-ranging appraisal of the literature on current and emerging PCa biomarkers with robust evidence to afford their application in clinical research and by extension routine clinical practice.

**Keywords:** prostate, cancer, biomarkers, diagnosis, prognosis, molecular, emerging, clinical

#### **1. Introduction**

Prostate cancer (PCa) is a complex condition characterized by varying clinical behaviors ranging from indolence to metastatic disease states. Globally, PCa was the second most prevalent cancer and the fifth major cause of cancer-related deaths among men in 2020. Strikingly, about 1.4 million new cancer cases and 375,000 deaths were attributable to PCa in 2020 [1]. Approximately one in nine men will be diagnosed with PCa in their lifetime [2]. Increased widespread screening using prostate specific antigen (PSA) mirrors the epidemic rise of PCa with geographic variability. However, since the advent of PSA screening, mortality rates have significantly declined [3]. The incidence of PCa in countries with low Human Development Index (HDI) was about three times lower than those with high HDI, 11.3 vs. 37.5 per 100,000 persons respectively [1]. The highest incidences were reported in the Caribbean, Northern and Western Europe, Australia/New Zealand, Southern Africa and North American regions [1]. The Caribbean and Sub-Saharan Africa accounted for the highest mortality rates [1]. There is mounting evidence that PCa disproportionately affects men of African ancestry. In the United States, African American men are 58% more likely to be affected by PCa with a 144% higher risk of PCa-specific mortality than their Caucasian counterparts [4]. The established risk factors of the disease include: increasing age, race and family history of PCa [5]. PSA is currently the most widely used screening tool for PCa indication, but a number of studies have highlighted its failure to discriminate between indolence and more aggressive forms of the disease. The low positive predictive value (PPV) of PSA has led to over-diagnosis of low-grade cancer and complications from unnecessary biopsies [6] as no cancer is detected on approximately 50% of biopsies [7].

Total prostate specific antigen (tPSA) is not very sensitive in detecting early PCa, and being cancer-specific as there are elevated levels in prostatitis, urogenital infections, BPH and transurethral manipulations. False positive results leads to increased rate of over-diagnosis, further expensive diagnostic evaluations and invasive procedures, and possibly over-treatment [8]. There are also false negatives particularly in the 'gray zone' (with tPSA values 4–10 ng/mL) resulting in undiagnosed PCa [9]. Furthermore, there is the absence of a linear correlation between serum tPSA and metastatic PCa as well as staging [10].

In order to increase the diagnostic utility of PSA for PCa, new biomarker such as Prostate Health Index (PHI), four K (4K) score and prostate cancer antigen 3 (PCA3) have become available. These tests decrease the number of needless prostate biopsies, provide valuable information on tumor aggressiveness and aid in the selection of PCa patients for radical therapy or active surveillance [11]. Genomic techniques have permitted the accessibility of novel genetic biomarkers such as transmembrane serine protease 2 (TMPRSS2:ERG fusion gene), Oncotype DX, Decipher and ProMark which stratify the risk of aggressive PCa and aid decision-making by providing information on diagnosis, prognosis and treatment [12].

This article seeks to review current advances in the development and availability of PCa biomarkers and their precise indications for diagnostic, prognostic, predictive and use in monitoring therapeutic response. Current and emerging biomarkers are appraised including their possible integration into medical practice and enhancing the clinical management of PCa.

#### **2. Method of article selection**

#### **2.1 Study eligibility criteria**

A literature search and review of recent publications in PubMed, Google Scholar, Embase and Cochrane library relating to the clinical utility of PCa biomarkers were

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

conducted. The search examined all relevant published studies up to January 20, 2022. The studies included in this review were published in peer-reviewed journals, written in English, and reported medical as well as scientific findings. Also, pertinent data that were extracted from published studies include: authors, year of publication and study design and population. Information from the articles concerning molecular biomarkers of PCa used for screening, diagnosis, prognosis, risk stratification and therapeutic tools were reviewed and documented.

#### **3. Results**

#### **3.1 Blood-based prostate cancer biomarkers**

#### *3.1.1 Prostate specific antigen*

Currently, the diagnosis of PCa is based on the results of digital rectal examination (DRE), trans-rectal ultrasound guided biopsy and PSA assay [13]. The aim of utilizing the least invasive methods has led to increased use of PSA as a biomarker. However, the use of PSA is limited by its low specificity, which in turn results in over-diagnosis of PCa followed by unnecessary biopsies and associated potential complications [14]. In the detection of PCa, serum total PSA (tPSA) > 4.0 ng/mL has a 94% sensitivity but only a 20% specificity, which makes the test unsuitable for screening [15]. There are also several limitations to PSA testing in the detection of high-grade PCa, with data from the Prostate Cancer Prevention Trial (PCPT) indicating that in order to achieve a sensitivity of 83.4%, a tPSA threshold of 1.1 ng/mL is required. However, the corresponding specificity was only 39.9% [16]. Since tPSA may be elevated in conditions other than PCa, only about 25% of men with elevated tPSA will be diagnosed, while there is still a 10% chance of patients with tPSA <1.0 ng/mL developing the disease [17]. In light of these limitations, several variations to the PSA assay have been implemented to increase specificity of the test. These includes: free PSA (fPSA), PSA velocity, free-to-total PSA (fPSA/tPSA) ratio and PSA density (PSAD). In addition to these PSA variation tests, prostate specific antibodies may also be useful in selecting men for biopsies especially since localized PCa may not present with symptoms [18].

PSA is typically found in the bound or free form. Bound PSA refers to PSA found complexed to protease inhibitors like alpha-1-antichymotrypsin, while fPSA which is found unbound is mostly inactive and associated with BPH as opposed to PCa [19]. A fraction of fPSA can however be active as such tPSA, refers to any of the active or inactive forms found in serum [20]. In light of these variations in PSA, specific assays targeting precise fractions have been developed. Bound or complexed PSA is found to be elevated in malignancies while the calculation of fPSA/tPSA ratio gives values that are normally lower in men with PCa [21, 22]. The fPSA/tPSA ratio is indicative of %fPSA and has been shown to increase the accuracy of PSA use in detection of PCa [23] (**Table 1**).

#### *3.1.1.1 PSA use in detection of prostate cancer*

Initial studies indicated that PCa was more likely with <25% fPSA at a sensitivity of 95%. However, subsequent similar studies have yielded unreliable results owing to the instability of fPSA making it a somewhat an unreliable marker on its own [54, 55]. This could be as a result of storage conditions as subsequent studies indicated that the stability of tPSA and fPSA levels in serum did not depreciate


#### **Table 1.**

*Summary of currently available biomarkers for use in prostate cancer screening, diagnosis, detection or stratification.*

significantly after 10 years storage at −80°C [29]. Other studies on tPSA and %fPSA have shown conflicting data as seen in the case of a 2009 study by Omar et al. [24]. Here, %fPSA values were high in patients diagnosed with PCa compared with BPH, while tPSA was found to be a better serum marker for diagnosing PCa in that cohort. However, a Chinese study highlighted that for men in the PSA gray zone, the inclusion of %fPSA will improve the diagnostic accuracy for PCa, and high grade PCa compared with only tPSA [25].

Clinical usefulness of PSA variations appears to be age-specific as a study comprising patients over 60 years old showed that tPSA outperformed %fPSA and therefore had the higher predictive value for detecting high grade carcinoma [20]. Moreover, an Asian study suggested that age-specific ranges for tPSA, fPSA and %fPSA could be considered in the diagnostic workup of PCa. Results from that study highlighted that there was a gradual increase in reference interval for tPSA and fPSA peaking at 7.73 and 2.41 ng/ mL respectively for those over 80 years old. The researchers also reported that reference intervals for %fPSA were ≥16.0 for 21–50 years and ≥13.0 for males over 50 years old [56]. In a South American study of over 17,000 men, %fPSA value of <15 as an indication for biopsy was shown to increase the rate of PCa detection in patients with normal DRE results and serum tPSA of 2.5–4.0 ng/mL [17].

Although the results vary with different studies, the PSA parameters mentioned seem to have significant predictive values in PCa screening and should be utilized based on the cohort along with other clinical factors in assessing PCa. This is in line with the conclusion by some researchers that especially within the gray zone, a

*The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

multivariate model is a useful tool in diagnosing PCa, and clinically significant PCa while reducing unnecessary biopsies in the process [18].

#### *3.1.2 Prostate cancer antigen-3 gene*

The thrust to discovering other biomarkers of PCa is based on the limitations that exist with regard to utilization of PSA or other invasive investigatory methods. Given the complications associated with PCa detection and treatment, the ultimate goal from a clinical perspective appears to be discovery of biomarkers that more accurately predict PCa and clinically significant disease prior to biopsy in an attempt to reduce the number of unnecessary biopsy requests [57].

Over time, some molecular biomarkers have been developed with emphasis placed on those that are capable of predicting disease aggressiveness that will lead to improved guidance for treatment modalities. Some of these markers are serum while others are urine-based.

To date, the most commonly used urinary biomarker of PCa is PCA3 otherwise referred to as Differential Display clone 3 (DD3). It is conventionally assessed in urine post prostate massage to derive the maximum number of prostatic cells, and was the first urinary RNA biomarker approved by the Food and Drug Administration (FDA) in 2012 [58]. Discovered around 1995 by researchers in the USA and The Netherlands, PCA3 is non-coding messenger RNA from chromosome 9q21-22, consisting of four exons and three introns and is over-expressed in most tested PCa tissues [59–61]. Additionally, PCA3 is over-expressed in prostate tumor tissue compared with other benign prostate disorders [13, 26]. It therefore aids in improving the accuracy of PSA with regards to management of early PCa and as such, approval was granted by some developed countries for its use as a molecular marker in the diagnostic workup of PCa owing to improved specificity for PCa over PSA [62, 63]. In fact, the use of PCA3 in tandem with other molecular markers including transmembrane serine protease 2 (TMPRSS2)-ERG, human kallikrein 2, and miRNA-141 was found to have significant clinical utility by way of increasing specificity, and in predicting PCa especially in the PSA gray area of 4–10 ng/mL [13].

Assessment of urinary PCA3 can be done by way of the quantitative real-time PCR (qRT-PCR) reaction followed by generation of a PCA3 score utilizing the ratio of PCA3 to tPSA [64, 65]. In utilizing this method, a negative biopsy result is 4.5 times more likely in men with a score of <25 compared with those with a score >25 [66]. Importantly, low-volume and low-grade disease are seen in men with low PCA3 scores [67]. A study on 407 high risk PCa patients indicated that PCA3 has clinical usefulness as a formidable prognostic indicator of tumor aggressiveness and was associated with higher PCA3 scores [27]. A high PCA3 score was also shown to be a good predictor of a positive PCa diagnosis and displayed greater accuracy than fPSA and tPSA in this regard (**Table 1**) [27, 28]. These results are in contrast to other studies indicating that the PPV of tPSA for diagnosing PCa was only 25% which directly increases the chances of obtaining false positive results in a quarter of cases and consequently a high chance of unnecessary biopsy in 75% of cases [57, 68, 69].

#### *3.1.3 Prostate health index*

The prostate health index (PHI) is a modern test that utilized PSA in accelerating the diagnosis of PCa. This particular biomarker has been sanctioned and approved in the United States, Europe and Australia. Studies conducted globally have depicted the reliably of PHI as a biomarker that outclasses its individual components for the

prognostication of overall and high-grade PCa on biopsy [70]. This represent PCa with Gleason score (GS) that is ≥7 [71].

In utilizing PHI as a biomarker, it allows for the combination of the tPSA, fPSA and [−2]proPSA (p2PSA) into a formula to produce a single result that can be used to assist in clinical decision-making of PCa. Such PHI formula computation is ([−2] proPSA/fPSA) × √PSA and demonstrates the possibility of clinically significant PCa in men with elevated tPSA and p2PSA while the fPSA is lower [70].

In men that possesses a tPSA between 4 and 10 ng/mL, PHI may be useful in establishing PCa in conjunction with a prostate biopsy. Interestingly, a low possibility of PCa outcome on biopsy is supplementary to small PHI results, while an elevated possibility of PCa outcome on biopsy is directly related to elevated PHI. Different medical considerations or family history of PCa are dominant factors in the management process as it relates to the appropriate PHI value [70, 72, 73].

A huge study was conducted in the USA in 2011 by Catalona et al. The study aimed to demonstrate the diagnostic capability of PHI for PCa recognition in a populace of 892 men with normal DRE, tPSA levels between 2 and 10 ng/mL and a prostate biopsy [73]. Interestingly, based on the PHI reference intervals, a value of 49 was obtained for prostate biopsies that were positive, while 34 was obtained for biopsies that were considered negative. This demonstrates a superior sensitivity and specificity in the diagnosis of PCa, and also differentiating PCa on biopsy compared with tPSA. Although the PHI test has been approved by the FDA only in the tPSA range of 4–10 ng/mL, PHI performed well in the 2–10 ng/mL range [74].

A large multicenter research involving approximately 5543 participants using the bivariate mixed-effect model was conducted by Zhang et al. from 2011 to 2019 to assess the medical significance of PHI in detecting PCa. The results obtained showed the likely sensitivity of PHI for diagnosing PCa of 0.75 and a specificity of 0.69. A value of 0.78 was obtained for the pooled area under the curve (AUC) and the diagnostic odds ratio (OR) was 6.73. The researchers also found that the diagnostic accurateness of PHI for PCa was greater in Asian compared with Caucasian populations (0.83 vs. 0.76). Based on the overall results the authors suggested that PHI has a modest diagnostic accurateness for detecting PCa [75].

Moreover, in a small study of 58 Asian patients with tPSA of 4–10 ng/mL who undertook transrectal ultrasound-guided prostatic biopsy, 18 cases had PCa and the AUC for this biomarker was 0.774. The sensitivity of PHI was 90% with a specificity of 27.5% which was the highest among the group of biomarkers including tPSA, PSAD and %fPSA. The authors suggested that PHI improves the accuracy of predicting PCa and decrease avoidable prostate biopsy [76]. These results are in consonance with another recent study involving 140 Korean patients that underwent prostate biopsy of which there were 63 cases of PCa. The AUC for PHI in the overall group was 0.76 (which was higher than tPSA, fPSA, %fPSA) and in the sub-group with GS ≥ 7, a value of 0.87 was obtained. PHI was a strong independent prognosticator of PCa particularly for the presence and aggressiveness (GS ≥ 7) of the disease, and its application could prevent a substantial amount of unnecessary prostate biopsies [77]. Furthermore, there are other recent studies that have demonstrated that PHI is more specific than tPSA in PCa detection [78] and a better predictor than %fPSA in detecting PCa at prostate biopsy [79].

#### *3.1.4 Four K score*

4K score test incorporates the measurement of four biomarkers: tPSA, fPSA, intact PSA, and human kallikrein-related peptidase 2 combined in an algorithm with the

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

patient's clinical information such as age, previous biopsy and DRE to generate the percentage risk (<1% to >95%) for aggressive metastatic PCa with GS > 7 on biopsy [31]. The 4K score test is usually conducted following a previous abnormal DRE or tPSA [31] and supports clinical decision making to determine whether a biopsy should be done. The 4K score has been found to predict high-grade metastatic PCa and estimates an individual's risk of having the disease that spreads to distant organs within 10 years [32]. This was supported by a multi-institutional prospective trial conducted in the United States among 144 men which sought to determine the association between previous 4K score, staging and grading of PCa at radical prostatectomy. It found that higher 4K scores were significantly associated with worse grades and aggressive histology. There was a higher median 4K score among PCa patients with cancer not confined to the prostate when compared with organ-confined cancer [36% (IQR 19, 58)] vs. [19% (IQR 9, 35)], (*p* = 0.002) [80].

In a clinical study involving 611 patients the 4K score test led to a 65% reduction in unnecessary biopsies [31]. There was also a strong association between high risk 4K score and a greater possibility of having a prostate biopsy. Similarly, another study with a population of 1012 men reported that the 4K score was useful in identifying candidates for biopsies with GS ≥ 7 and could narrow the gap of unnecessary biopsies by 30–58% [7]. However, the researchers highlighted that about 1.3–4.7% of men with aggressive disease may experience a delay in diagnosis using the 4K score [7]. Moreover, a retrospective study performed on 946 men of different racial ethnicities with elevated tPSA levels and a previous biopsy demonstrated that the 4K score had a higher discriminatory index for high-grade PCa compared with the conventional tPSA, DRE, age and PCPT calculator [31]. The researchers showed that among African American men, the detection of metastatic PCa using the 4K score test was significantly enhanced over the use of tPSA with an AUC of 0.80 versus 0.67. Additionally, it was found that the 4K score test would be able to identify 88% of aggressive cancers while reducing 42% of unnecessary biopsies [81].

#### **3.2 Urine-based prostate cancer biomarkers**

#### *3.2.1 TMPRSS2-ERG fusion and PCA3*

In a similar way to PCA3, a transmembrane serine protease 2 (TMPRSS2)-ERG fusion gene can be detected in urine post-DRE [82]. Various biomarkers are released in urine which may become enriched with prostate material upon manipulation of the prostate gland during a DRE [83]. A TMPRSS2-ERG gene fusion score is comparable to PSA mRNA quantity, in which the latter is currently being used as the gold standard test for PCa. TMPRSS2-ERG fusion gene is a genetic rearrangement of the androgen-regulated trans-membrane protease, serine 2 (TMPRSS2) gene and the ERG (erythroblast transformation-specific; ETS)-related gene. ERG is an oncogene and is a part of the family of transcription factors. TMPRSS2-ERG gene fusion is expressed specifically in PCa and is the most prevalent known type of PCa-specific gene alterations [84, 85]. There are two mechanisms by which TMPRSS2-ERG gene fusion may occur, either by chromosomal translocation or interstitial deletion; the latter being the more predominant mechanism in which approximately 2.8 Mb genetic material may be lost due to occurrence of this event [86–89]. A study reported that the deletion type fusion was found to be highest among African American patients, followed by Caucasians and no significant differences have been seen in Asian populations regarding either type [89]. Targeted inhibition of the TMPRSS2-ERG fusion

gene or its gene fusion transcripts could possibly serve as a treatment strategy in the future, thereby resulting in favorable outcomes for PCa patients.

Gene fusion of TMPRSS2 and ERG has been shown to result in an overexpression of ERG. ERG has been shown to play a vital role in cell growth, differentiation, and apoptosis [90]. It is possible that overexpression of ERG triggers a downstream cascade of events that lead to the onset and progression of PCa. Hence, TMPRSS2-ERG gene fusion maybe seen as an early phenomenon that takes place in the development of PCa. TMPRSS2-ERG gene fusion has been detected in 40–70% of PCa patients [88]. The frequency of TMPRSS2-ERG gene fusion has been reported to be greatest among Caucasian Americans (50%), followed by African Americans (31%) and Asians (18.5%) respectively [89].

TMPRSS2-ERG and PCA3 are two of the most studied urine biomarkers with PCA3 having received FDA approval. TMPRSS2-ERG and PCA3 in post-DRE urine for the detection of PCa at biopsy showed significant improvement over PSA [85, 91, 92]. When a Mi-Prostate Score for post-DRE urine, was utilized it was reported that TMPRSS2-ERG had a low sensitivity of 24.3–37.0%. However, the fusion gene had a specificity of 93% and a PPV of 94%. Combination of TMPRSS2-ERG with serum tPSA (a cut-off value of 10 ng/mL) and urinary PCA3, greatly improves the accuracy of diagnosing PCa, from a study that reported a sensitivity of 80% and a specificity of 90% [93]. TMPRSS2-ERG gene fusion test also gives information about a risk assessment for aggressive PCa [94].

It was reported in a study that TMPRSS2-ERG gene fusions showed a significant association with a GS ≥7 and PCa-related deaths. When TMPRSS2-ERG and PCA3 were combined with the PCPT risk calculator, the information provided may aid physicians in deciding whether a patient with high serum tPSA will need urgent biopsy [91, 94]. Analyzing ERG mRNA in post-DRE urine in a study cohort of 237 men, a predictive accuracy for AUC of 0.80 was reported for PCa diagnosis in Caucasian men having tPSA levels ≤4.0 ng/mL [92]. Studies have supported the combined use of TMPRSS2-ERG and PCA3 in clinical practice to help in reducing the number of prostate biopsies [95]. With this in mind, Kohaar et al. concluded that the data from cumulated studies suggest that when TMPRSS2-ERG and PCA3 are combined along with serum tPSA there were improvements in the detection of aggressive PCa (GS ≥7) on initial biopsy with a 42% reduction in unwarranted biopsies [85, 91].

#### *3.2.2 SelectMDx (DLX1, HOXC6)*

The SelectMDx test is a urine-based gene expression assay. This assay measures the mRNA levels of two biomarkers, distal-less homeobox 1 (DLX1) and homeobox C6 (HOXC6) using qRT-PCR in post DRE urine [96]. Kallikrein serine protease (KLK3) gene which codes for PSA is used as an internal control for this assay. The test was performed on patients who have risk factors for PCa and were being considered for prostate biopsy. DLX1 and HOXC6 are believed to be involved in the onset of PCa and are both associated with high grade disease [95]. In a study using PCa cell lines, it was found that HOXC6, a transcriptional factor when suppressed, caused a reduction in cell viability and induced apoptosis [97, 98]. In another study investigating the role of DLX1, a protein coding gene, it was reported that DLX1 promoted growth, migration and colony formation of cancer cells [97, 99].

The SelectMDx assay is not a "standalone" test and so incorporates clinical factors such as age, tPSA, prostate volume and DRE findings to estimate the percent likelihood of detecting PCa and high-grade (GS ≥ 7) disease upon prostate biopsy. Leyten et al. [30] first identified that a three gene panel of HOXC6, DLX1 and

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

Tudor domain-containing protein 1 (TDRD1) was able to show higher accuracy (AUC = 0.77) for the detection of clinically significant PCa (csPCa) compared with tPSA (AUC = 0.72) and PCA3 (AUC = 0.68) tests respectively. In follow-up prospective studies, focus was placed on the urinary mRNA levels of HOXC6 and DLX1 genes. Using a large cohort (*n* = 905), the expression of DLX1 and HOXC6 gave an AUC of 0.76, a sensitivity of 91%, a specificity 36%, a NPV of 94% and PPV of 27% for the prediction of high-grade PCa (**Table 1**) [100]. It was seen that when SelectMDX was combined with clinical factors such as tPSA, PSAD, family history and history of prostate biopsy, the risk stratification of high-grade PCa and biopsy decisions maybe improved as the AUC was 0.90 in identifying high-grade PCa (GS > 7) [100].

In a more recent validation study by Haese et al. [101], the data showed high sensitivity and NPV in detecting csPCa when investigating urinary HOXC6 and DLX1 mRNA levels. These results were combined with patient age, DRE and tPSA levels less than 10 ng/mL which gave an AUC of 0.82, sensitivity 89%, specificity 53% and NPV 95% [102]. Haese et al. [102] concluded that the data supported using the SelectMDx test to aid in decision-making around prostate biopsies. Furthermore, Govers et al. [101] conducted a study on the healthcare cost using SelectMDx for PCa in four European countries. From the results of the study, it was reported that qualityadjusted life years (QALYS) could be gained and that the use of SelectMDx may have favorable economic outcomes for patients at initial PCa diagnosis. Currently, SelectMDx is only available through companies that received CLIA (Clinical Laboratory Improvement Amendments)-approval [102].

#### **3.3 Tissue-based prostate cancer biomarkers**

#### *3.3.1 Oncotype DX Genomic Prostate Score*

Oncotype DX Genomic Prostate Score (GPS) is considered a molecular biomarker that was established to aid in the prognostication of PCa in men with intermediary possibility of the disease. This particular biomarker is based on 17 genes GPS. This can be further explained where 12 of the genes referred to as qRT-PCR genes are responsible in identifying growth linked to PCa, while the remaining five genes are responsible for demonstrating stromal response, androgen signaling, cellular organization, and proliferation, thereby achieving a computational formula system that resulted in the GPS [103]. This particular Oncotype DX GPS assay measures mRNA expression of the 17 genes accountable for neoplasm progression and was established and reviewed in approximately 4500 patients [104].

A study was conducted to identify and authenticate a biopsy-based 17-gene GPS signature by investigating 732 candidate genes in their clinical utility to predict PCa mortality, adverse pathology and clinical recurrence. The GPS predicted high-grade PCa and clinical recurrence notwithstanding multi-focality and heterogeneity. The authors suggested that the GPS test assist patients in making knowledgeable decisions concerning immediate therapy or active surveillance [103]. In another study, Cullen et al. assessed the association of the 17-gene GPS with clinical recurrence in 431 men with clinically low to intermediate risk PCa. GPS results (scale 0–100) were obtained for 402 PCa patients and it predicted time to metastases and biochemical recurrence. GPS was significantly associated with adverse pathology (OR = 3.3 per 20 GPS) and the predictive outcomes were similar for Caucasian and African American men [43]. Recently, the same authors performed a multicenter comparison of 17-gene Oncotype DX® GPS in Caucasian (*n* = 1144) and African American (*n* = 201) men diagnosed

with clinically localized PCa. The GPS scores were the same between the two racial groups showing corresponding predictive outcomes, and using a multivariate model, biochemical recurrence and adverse pathology was significantly associated with the GPS assay (**Table 1**) [44]. Supporting evidence of Oncotype DX GPS as an independent predictor of adverse pathology in the two racial groups was provided by Murphy et al. [45] using PCa patients (96 African American and 76 European American men) from two multi-institutional observational studies.

There are other studies such as that performed by Kornberg et al. [46] which found that higher GPS in PCa patients who undertook radical prostatectomy following active surveillance is associated with greater risk of adverse pathology and biochemical recurrence. Lynch et al. [47] reported that GPS testing was a valuable tool in risk stratification among PCa patients and those who are low risk are more likely to make the decision to adopt active surveillance. Notably, Chang et al. posited that the deployment of GPS was worthwhile in guiding decisions regarding therapy in patients with early stage PCa compared with active surveillance [48]. The finding that the GPS test is associated with long-term outcomes such as PCa-specific mortality and distant metastases is also worth mentioning [105]. However, in a recent study there was no significant association of the GPS test with adverse pathology after initial period of neither active surveillance nor improvement in risk stratification for adverse pathology versus the use of only clinical variables [106].

#### *3.3.2 The Decipher*

The Decipher, a molecular biomarker is categorized as a genomic assessment, established by GenomeDx Biosciences in Vancouver, Canada. This particular test evaluates the expression signature of approximately 22 RNA genes that demonstrates the prognostication and progression of PCa. Among the various genes group, the Decipher in recent time is considered the most powerful method in that it comprises of a comprehensive transcriptome investigation of a prostatectomy, biopsy, or transurethral resection specimens. Interestingly, the Decipher method for evaluation of PCa prognostication was initially authenticated in radical prostatectomy patients with uncomplimentary specimen characteristics inclusive of positive cancer margins and is however independent of clinical data [33–35, 107]. A study by Den et al. found that the Decipher method demonstrated both biochemical recurrence and cancer spreading in approximate 139 participants after removal of the prostate in addition to radiotherapy [36].

Several authors have done in-depth assessment and evaluation of the clinical utility of the Decipher method in PCa progression. Spratt et al. performed a meta-analysis of 5 studies comprising 855 patients in assessing the performance of the Decipher test in PCa patients who underwent radical post-prostatectomy. The Decipher test classified patients as low, intermediate and high risk for developing metastases and was a significant predictor of metastasis (HR = 1.30, *p* < 0.001). The authors posited that the Decipher test can increase the prognosis of PCa patients after radical prostatectomy including those in the different clinicopathologic and therapy subgroups (**Table 1**) [33]. Similarly, the prognostic potential of the Decipher test was assessed in two highrisk USA and European case-control studies. The median Decipher scores were higher in PCa patients who developed metastases, and multivariate analysis showed that there was a greater risk of distant metastases for each 10% increase in Decipher score within a 10-year follow-up period. Therefore, the Decipher test predicted metastatic recurrence in PCa patients within a follow-up period of 10 years [37]. In a retrospective multicenter cohort study comprising of 266 PCa patients, the Decipher from

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

prostatectomies from PCa patients with low to intermediate risk predicted the absence of adverse pathologic features thus making these individuals suitable for active surveillance [38]. In a later study involving prostatectomies from 2342 PCa patients, the Decipher score was positively correlated with baseline tumor characteristics such as age, pathologic T-stage and GS [39]. The Decipher score was able to reclassify patients according to tumor aggressiveness and may be valuable in assessing postoperative risk and decision making [39].

There are other studies that have assessed the clinical usefulness of the Decipher test in risk stratification of newly diagnosed PCa patients. In a multicenter prospective study involving 855 persons who underwent Decipher Biopsy testing, a high-risk Decipher score for PCa patients in the active surveillance group was independently associated with time to treatment while the same was related to time to failure in the radical therapy group [40]. Likewise, in a study of 203 PCa cases, the Decipher score enables risk stratification and was significantly associated with time to biochemical recurrence. The Decipher score could assist PCa patients with treatment decision as high-risk values were significantly associated with salvage treatment [40]. Interestingly, a systematic review conducted recently of 42 studies and 3407 patients [localized, post-prostatectomy, metastatic castration resistant PCa (mCRPCa) and metastatic hormone sensitive PCa (mHSPCa)], and metastatic hormone sensitive PCa (mHSPCa), the Decipher test was robust for intermediate-risk PCa and decisionmaking after radical prostatectomy [41].

The Decipher test has been used in risk stratification of early diagnosed PCa patients and for treatment making decisions [108]. Dalela et al. investigated the use of the Decipher test in a cohort of 512 PCa patients as a valuable risk-stratification tool for identifying those persons who would be received maximum benefit from adjuvant radiotherapy (ART). The Decipher test was one of the parameters used to develop a Multivariable Prediction Model that predicted reduced risk for clinical recurrence. Using the Decipher test, the authors suggested that ART might decrease overtreatment and needless adverse effects [34]. Also, supporting evidence was observed in the Multicenter Prospective PRO-IMPACT study comprising of 150 PCa patients where the Decipher score acts as a guide for making treatment choice, and enhance the effectiveness of the decision-making process for PCa patients considering salvage radiotherapy (SRT) or ART post-radical prostatectomy [35]. The use of the Decipher test has been found to significantly improve therapy decision-making in a study published of two prospective registries of PCa patients [42]. Of particular interest is a study by Lobo et al. which based on the findings suggests that the Decipher test was a cost-effective approach to PCa treatment decisions after radical prostatectomy. This should result in improved clinical outcomes and the potential for the application of the Decipher test for personalized cancer medicine [109].

#### *3.3.3 ConfirmMDx*

ConfirmDx is a tissue based biomarker that is used to determine the likelihood of a true negative biopsy versus one that has an occult cancer. The aim of this test is to prevent unnecessary repeat biopsies and also to detect those patients with negative biopsies who in fact, do require repeat biopsies. Unnecessary biopsies put patients at risk for complications from the biopsy procedure and increases morbidity, in addition to increasing economic burden.

Prostate biopsies are done when there is an increase in tPSA level and/or abnormal prostate DRE. When a prostate biopsy is negative and there is a high suspicion of

PCa, the residual tissue from the biopsy can be submitted for the ConfirmMDx test. ConfirmMDx is an epigenetic assay that is used to detect DNA hyper-methylation changes. Epigenetic changes such as DNA methylation have been implicated in the molecular pathogenesis of PCa. These changes occur surrounding the tumor foci, called the halo effect. It occurs within a DNA sequence, when a methyl group is added to a cytosine nucleotide that is adjacent to a guanine nucleotide [110]. The three genes tested for DNA hyper-methylation associated with PCa are: GSTP1, APC, and RASSF1 genes [110]. Unlike histopathology of a prostate core biopsy which would have missed the diagnosis of epigenetic changes, these 3 genes have the potential to expose the presence of tumor activity via the use of ConfirmMDx.

From the methylation analysis to locate occult cancer (MATLOC) study conducted in 2013, the rate of false negative results from prostate biopsies was significantly lowered with a 90% NPV when compared with histopathology. Specificity and sensitivity were found to be 64% and 68% respectively [111]. Moreover, another study to substantiate the MATLOC study was the DOCUMENT (detection of cancer using methylated events in negative tissue) trial done in 2013. This resulted in a NPV of 88% (95% Cl 85–91) [49]. This was significant for repeat biopsies as an independent predictor of PCa.

In another study, Waterhouse et al. in 2018 found that ConfirmMDx used for PCa detection had a sensitivity and specificity of 74.1% and 60.0% respectively at repeat biopsies. The study validated the use of ConfirmMDx in African American men, and was significant as most studies were done on a Caucasian population [50]. Further, Wojno et al., found that PCa patients who were managed using ConfirmMDx test had a <5% rate of repeat prostate biopsies. Compared with previous rates, there was a tenfold reduction [51]. ConfirmMDx test has the potential therefore, to reduce healthcare costs by avoiding unnecessary repeat biopsies and to also avoid the morbidity associated with prostate biopsies. Notably, ConfirmMDx is not FDA approved.

#### **3.4 Emerging molecular prostate cancer biomarkers**

#### *3.4.1 The ProMark*

There are challenges in defining the aggressiveness of PCa as well as its outcome (particularly lethality) using prostate biopsy as there are sample errors and disparities in interpretation. Shipitsin et al. documented a proteomic biopsy-based PCa prognostic advanced test panel called ProMark that is manufactured and distributed by Metamark Genetics Incorporated, USA. They performed and documented a result-orientated method that provides an accurate prognosis of PCa aggressiveness and lethal outcome irrespective of variation in biopsy sampling [112]. Using a large patient cohort, prostatectomy tissue samples were identified and classified as having lowest to the highest Gleason grade. Tissue microarrays were produced comprising of cores from low as well as high Gleason area from each PCa patient. An assessment of 160 known protein biomarkers was carried out by means of using a quantitative multiplex proteomics in situ imaging system and a selection strategy with three types of criteria namely biological, technical and performance-based. Analytical performance and the application of univariate and multivariate analyses resulted in a final set of 12 protein biomarkers which provided prognostic accuracy of tumor behavior [113]. The same researchers conducted further investigations with the subsequent selection of 8 of these 12 protein biomarkers in a prognostic model that offered "risk scores" predictive of the final post-prostatectomy pathology.

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

The eight proteomic biomarkers that constitute ProMark are: HSPA9, YBX1, CUL2, PDSS2, pS6, FUS, DERL1 and SMAD4 [52].

Blume-Jensen et al. performed a multicenter 8-protein biomarker assay model investigation involving 381 PCa patient biopsies with corresponding prostatectomy specimens. This was followed by a second blinded study of 276 cases which validated the 8-protein biomarker assay model's capacity to differentiate "non-favorable" versus "favorable" pathology in a manner that was independent and comparative to D'Amico and National Comprehensive Cancer Network (NCCN). The protein biomarker panel of ProMark gives a risk score ranging from 0 to 1 and predicts the aggressiveness of PCa and lethal outcome in patients with GS of 3 + 4 and 3 + 3 on biopsy. There was a false positive rate of 5% that corresponds to a non-favorable protein biomarker assay risk score >0.80 and false negative rate of 10% which relates to favorable protein biomarker assay risk score <0.33 [52]. The ProMark predictive model gave values for favorable pathology (risk score ≤ 0.33) of 87.2% for patients in the low-risk D'Amico group, as well as 81.5% and 95.0% for those in the low-risk and very low-risk NCCN groups respectively. These predictive values were higher than those of the up-to-date risk classification groups (70.6%—low-risk D'Amico group; 63.8%—low-risk NCCN; 80.3%—very low-risk NCCN respectively). The ProMark predictive model gave a value for non-favorable pathology (risk score > 0.80) of 76.9% for all the NCCN and D'Amico risk groups. The validation study of the 8-protein biomarker assay predictive model was able to distinguish non-favorable from favorable (AUC = 0.68; p < 0.0001; OR = 20.9) and GS 3 + 3 versus GS 3 + 4 (AUC = 0.65; *p* < 0.0001; OR = 12.95) [52]. Overall, with increasing ProMark biomarker risk scores there was reduced frequency of favorable PCa cases across all the D'Amico and NCCN risk groups [52].

#### *3.4.2 miRNAs*

#### *3.4.2.1 Diagnosis, progression, risk stratification and therapeutic potential of serum or plasma miRNAs*

MicroRNAs (miRNAs) are minute single-stranded as well as non-coding sections in RNA comprising of approximately 22 nucleotides that pay a critical role in gene regulation [53]. In the past decade a number of studies have investigated the differential expression and levels of miRNAs in plasma or serum in order to develop non-invasive blood-based biomarkers with the ability to diagnose, detect progression and assess prognosis as well as recurrence of PCa [114]. Jin et al. investigated 10 serum-circulating miRNAs as non-invasive molecular biomarkers in 31 BPH and 31 PCa patients. The expression levels of miR-375, miR-200b, and miR-141 levels were significantly elevated in the PCa patients compared with those in the BPH group, and miR-200b was the most effective diagnostic marker with AUC = 0.923 [115]. An association was found between the three miRNAs and tPSA, as well as miR-200b and GS [115]. The upregulation of miR-141 was also observed in a study by Ibrahim et al. that comprised 80 PCa (30 metastatic and 50 localized), 30 BPH patients and 50 healthy controls (**Table 2**). Plasma miR-141, miR-221, miR-18a and miR-21 levels were significantly higher in PCa patients than healthy controls. miR-18a differentiate PCa from healthy individuals with the highest AUC of 0.966, while miR-221 has a sensitivity of 92.9% and specificity of 100% at differentiating localized from metastatic PCa [116]. Likewise, another study differentiated PCa from BPH as higher significant expressions of the two onco-mRNAs miR-375-3p and miR-182-5p were found in the plasma of PCa compared with BPH patients (specificity = 90.2%) [131]. Similarly, the expressions levels of miR-375-3p


#### **Table 2.**

*Summary of miRNA expression studies on plasma or serum samples from prostate cancer patients.*

and miR-182-5p were evaluated for their diagnostic and prognostic potential in a cohort of 98 PCa and 52 normal controls. The plasma miR-182-5p expression level was significantly higher in PCa patients compared with controls, and it detected the disease with AUC = 0.64 (specificity of 77% and NPV of 99%). The authors also found that the levels of both miRNAs were associated with higher GS, and miR-182-5p was significantly elevated in metastatic PCa [132].

miRNAs exhibited both diagnostic and prognostic ability and these could be considered for possible use in clinical practice [108]. Cai and Peng investigated the diagnostic potential of miR-494 in a study comprising 90 BPH and 90 PCa patients,

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

and 90 healthy controls. The serum expression of miR-494 was significantly higher in PCa compared with BPH patients and healthy controls, and positively correlated with GS, tumor size and stage, and serum tPSA levels. miR-494 was suggested to be a sensitive biomarker of PCa as the AUC was 0.809 [117]. A similar investigation was carried out for miR-301a extracted from serum and tumor samples in a study design involving two cohorts (cohort 1 of 13 BPH and 25 PCa, and cohort 2 of 12 BPH and 13PCa). miRNA-301a expression in serum and tissue was significantly higher in PCa compared with BPH patients, and there was correlation with increased GS for miR-301a in radical prostatectomy specimens [118]. Also, miR-410-5p was investigated as a potential serum biomarker for PCa in a study comprising 149 PCa and 121 BPH patients, and 57 healthy controls. The serum expression of miR-410-5p was significantly elevated in PCa compared with BPH patients or healthy controls. The diagnostic accuracy of serum miR-410-5p indicated by an AUC of 0.810 suggests that it is a potential molecular biomarker for the diagnosis of PCa [119].

Other miRNAs have been overexpressed or under-expressed in circulation thus demonstrating their diagnostic and prognostic potentials. Lieb et al. in a study of 145 PCa and BPH patients, and 19 healthy controls reported that the serum levels of miRNA family members (miR-320a, miR-320b and miR-320c) differed among the three groups been highest in the PCa patients. In addition, the serum levels of all three miRNAs were significantly higher in older patients, high tumor stage and those with tPSA >4 ng/mL [120]. Conversely, decreased miR-128 expression was found in the serum of 128 PCa patients which was associated with disease progression and short biochemical recurrence-free survival (**Table 2**) [121].

miRNA profiling experiments followed by validation showed decreased expression of serum miR-25, miR-628-5p, and miR-101 in African American and Caucasian Americans with PCa compared with healthy controls [122]. Other serum or plasma miRNAs which have been identified as potential non-invasive biomarkers for PCa include: has-miR-101-3p and has-miR-19b-39 (diagnosis and prognosis) [133], miR-940 (diagnosis, AUC = 0.75) [134], panel of miR-27a-3p, miR-424-5p, miR-29b-3p, miR-4286 and miR-365a-3p (detecting early stage PCa) [123], panel of miR-126, miR-377, miR-15a and miR-192 (detection of localized PCa and risk stratification) [124] as well as panel of miR-373, miR-141, miR-21, miR-125b, miR-126, miR143 and let-7b (diagnosis of metastatic PCa) (**Table 2**) [125].

#### *3.4.2.2 Diagnosis, prognosis and therapeutic potential of miRNAs in tissue*

The profiling of miRNAs in tissues of PCa patients shows pattern that are different compared with those from healthy controls. These distinct miRNA expressions in PCa tissues could afford tools for improved diagnosis, prognosis and therapeutic approaches for PCa [114]. Huang et al. investigated the clinical utility and prognostic potential of hsa-miR-30c and hsa-miR-203 in tissues of 44 PCa patients. The expressions of the two miRNAs in tumor tissues were significantly different from those in neighboring normal tissue indicating their diagnostic potential. All the PCa patients were followed up for 36 months and the data showed that the mean survival times of high and low expressions of hsa-miR-203 and has-miR-30c respectively were significantly lower, which attest to their possible prognostic utility for PCa [135]. In a study involving tissue samples from 14 BPH and 60 PCa patients (cancerous and noncancerous prostate samples) and the employment of qRT-PCR followed by validation, the expression levels of 4 miRNAs (miRNA-141-5p, miR-183-5p, miR-32-5p and 187-3p) differed significantly between PCa and BPH samples [136]. The data suggests that these four miRNAs

could detect cancer in prostate biopsy as they were able to differentiate between malignant and nonmalignant prostates [136]. Likewise, the expressions of miR-27b were higher in PCa compared with BPH tissues, and correlated with GS and clinical stages in PCa. The researchers further posited that PCa patients with higher expression of miR-27b had worse progression free as well as overall survival [137]. There are other studies that have shown an overexpression of miR-153 in PCa tissue which was associated with worse overall survival (**Table 3**) [145], and the deregulation of miR-30c and miR-29b with decreased expression in PCa tissues and ability to differentiate between PCa and adjacent para-cancerous tissues (AUC = 0.944 for miR-30c and AUC = 0.924 for miR-29b) (**Table 3**) [138].

There are a few studies that have investigated the therapeutic potential of miRNAs for PCa treatment because of their ability to bind targets using prostate tissue and cell lines [149]. Wang et al. explored the prognostic potential and possible use of miR-1231 as a therapeutic tool by measuring its expression levels in PCa tissues. The miR-1231 expression was decreased in PCa tissues and significantly associated with shorter overall survival, higher TNM stage, lymph node metastasis and higher clinical stage. The data also showed that epidermal growth factor receptor (EGFR) is a target


#### **Table 3.**

*Summary of miRNA expression studies on tissue from prostate cancer patients.*

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

of miR-1231 and its over-expression reduced migration, proliferation and invasion of PCa cell lines in vitro. Based on the evidence, the researchers suggested that miR-1231 has a tumor-suppressive role, may be a prognostic biomarker, and a therapeutic tool for PCa treatment in the future (**Table 3**) [143]. In another study involving naïve radical prostatectomy specimens from 535 PCa patients, decreased expression of miR-424-3p was significantly associated with the aggressive phenotype of PCa and clinical failure-free survival. Based on the evidence, the authors posited that miR-424-3p could be a possible target for treatment of PCa (**Table 3**) [139].

#### *3.4.3 Exosomes*

Exosomes are minute membrane-bound extracellular vesicles with diameters of 30–150 nm. They are released from many cell types into body fluids and the extracellular environs after the amalgamation of multi-vesicular bodies fuse with the plasma membrane [150]. Exosomes comprised of a number of cytoplasmic biomolecules such as lipids, glycol-conjugates, proteins, DNA and RNA including miRNAs surrounded by a lipid bilayer membrane. They are found in blood (plasma and serum), urine, saliva and semen and cell culture medium [140, 151]. During PCa development and metastasis, exosomes are secreted by tumor cells and have been reported to play a critical role in initiation, promotion, immuno-regulation, angiogenesis, annexation and metastasis [152]. As exosomes play a role in PCa development via a number of mechanisms of actions, they are valuable biomarkers for PCa diagnosis, prognosis and monitoring [153, 154].

#### *3.4.3.1 Exosome-contained microRNAs*

Exosomal miRNAs are small-stranded non-coding RNA (about 17–25 nucleotides in length) and changes in their concentration in body fluids make them useful molecular biomarkers of PCa progression [155]. There is increasing interest in exosomal miRNAs in serum and plasma samples due to their stability and as non-invasive molecular biomarkers for PCa diagnosis and recurrence [156]. Li et al. conducted a study comprising 31 PCa patients and 19 healthy persons and found that plasma exosomal miR-125a-5p levels were significantly decreased in the former compared with the latter. The researchers also found that plasma exosomal miR-141-5p levels mildly increased in PCa patients, and the miR-125a-5p/miR-141-5p ratio was able to distinguish these patients from healthy controls (AUC = 0.793) [157].

In a recent study, Guo et al. examined the predictive potential of 6 plasma exosomal miRNAs in a first validation prospective cohort of 42 CRPCa and 108 treatment-naive PCa patients, and found that miR-423-3p was associated with CRPCa (AUC = 0.784). In a second validation study reported by the same authors, plasma exosomal miR-423-3p expression was significantly higher in 30 CRPCa patients undergoing androgen depletion treatment compared with 36 non-CRPCa patients (AUC = 0.879). The authors suggested that plasma exosomal miR-423-3p may serve as a useful molecular biomarker for early diagnosis and prognosis of castration resistance in PCa patients [153]. Moreover, in an earlier study that examined the overall survival in a prospective cohort of 23 CRPCa patients, significantly elevated levels of plasma exosomal miR-375 and miR-1290 were associated with worse clinical outcome and overall survival in CRPCa patients [158].

There are serum exosomal miRNAs that are investigated for potential clinical utility for PCa. In an investigation by Li et al., the expression of serum exosomal miR-141 was

significantly higher in PCa patients compared with those of BPH patients and healthy individuals. Serum exosomal miR-141 expression was also significantly higher in PCa patients with metastasis compared with localized disease. The authors suggested that serum exosomal miR-141 could be a valuable molecular biomarker for the diagnosis of metastatic PCa [159]. In preclinical vivo and in vitro studies, exosomal miR-1246, a tumor suppressor was downregulated in PCa cell lines, and clinical tissues correlated with the presence of metastasis, poor prognosis and increasing GS [160].

Urinary exosomal miRNAs have been found to be valuable noninvasive and diagnostic molecular biomarkers of PCa [161]. Shin et al. investigated the clinical utility and profiles of urinary exosomal miRNA expressions in 149 PCa cases and the identification of those associated with metastasis. Urinary exosomal miR-21 and miR-451 expressions were upregulated and miR-636 downregulated in metastatic PCa patients compared to those with localized disease. These three exosomal miRNAs were used to develop a Prostate Cancer Metastasis Risk Scoring (PCa-MRS) model for PCa patients with high scores showing significantly worse biochemical recurrence-free survival [162]. Similarly, in a recent study comprising of a next-generation sequencing cohort (6 PCa patients and 3 healthy individuals) and use of qRT-PCR (28 BPH patients, 47 PCa patients and 25 gender- and age-matched healthy controls), urinary exosomal miR-486-5p, miR-486-3p, miR-375, miR-486-5p and miR-451a expressions discriminated PCa patients from healthy controls with AUCs ranging from 0.704 to 0.796. The researchers found that urinary exosomal miR-375 differentiated metastatic PCa from localized (AUC = 0.806), and miR-451a along with miR-375 distinguished localized PCa from BPH (AUC = 0.726) [155]. In an earlier study comprising 90 PCa patients, 10 BPH and 50 healthy controls, urinary exosomal miR-2909 was reported to be a non-invasive diagnostic molecular biomarker of PCa and disease aggressiveness [163]. Preclinical studies also revealed that urinary exosomal miR-574-3p and miR-375 were detected by molecular beacons in PCa cells such as PC-3 and DU145 [164]. Furthermore, urinary exosomal miR-532-5p expression was upregulated in 26 PCa patients with biochemical recurrence and exhibit poor prognosis in those with intermediate-risk disease [165].

The findings from these observational studies indicated that circulating exosomal miRNAs in serum, plasma or urine may assist in the diagnosis, prognosis and outcomes of PCa and could be adopted into routine clinical practice in the near future.

#### **3.5 Potential prostate cancer biomarkers in development**

#### *3.5.1 Prostarix*

Prostarix is another PCa biomarker that has potential value in screening/diagnosis. It is a post-DRE urine test that is used to predict the likelihood of having a negative prostate biopsy versus one with occult PCa. This is done using a risk score. The risk score is generated using an algorithm which ranges from 0% to 100%. One hundred percent equates to a 100% chance of having PCa on a prostate biopsy. Therefore, it is used to determine which men will undergo prostate biopsy. Prostarix is used when there is an elevated tPSA and normal DRE, and in some cases, for men who are candidates for a repeat prostate biopsy [166, 167].

Prostarix measures four metabolites using liquid chromatography mass spectrometry. These metabolites are sarcosine, alanine, glycine and glutamate. Sarcosine especially, has been linked with PCa progression [168]. A study conducted by McDunn et al. 2013, showed that these metabolites improved prediction

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

of organ confinement (AUC, 0.53–0.62) and 5-year recurrence (AUC, 0.53–0.64) [166]. However, a study done by Sroka et al. in 2017 that evaluated various amino acids as PCa biomarkers found that sarcosine was not a definitive indicator of PCa when analyzed in pre and post-prostate massage urine samples. Also, the amino acids arginine, homoserine, and proline were mostly seen in the urine samples of PCa patients as opposed to patients with a benign prostatic disease [169]. It is evident that more studies need to be done on Prostarix, and of note, it is not FDA approved.

#### *3.5.2 Prostate Core Mitomic Test*

Prostate Core Mitomic Test (PCMT) is a tissue based biomarker for detecting PCa. It is an RT-PCR test that detects large-scale mitochondrial deletions in prostate biopsy samples. It is able to identify tumor activity at the molecular level and detects molecular changes that occur at the mitochondrial DNA (mDNA) level. The large-scale deletion of 3.4 kb of the mitochondrial genome has been known to occur as a part of the prostate "cancerization" field effect [170]. Cancerization field effect occurs when cells adjacent to primary tumors become transformed [171]. This takes place at the start of oncogenesis. Cancerization field effect is seen in some type of cancers including PCa. Histopathology would have otherwise labeled these prostate biopsy samples with cancerization field effects as "normal" appearing tissue.

PCMT is used to predict repeat biopsy outcomes with an initial negative biopsy. This test can therefore, aid in reduction of the number of unnecessary biopsies and also reduce health care costs. According to Robinson et al. in 2010, the sensitivity and specificity of this mitochondrial deletion in predicting repeat biopsy outcomes were found to be 84% and 54% respectively. It also showed a NPV of 91% and an AUC of 0.749 [172]. Legisi et al. performed a multicenter observational study to assess the use of the PCMT in the decision making of repeated biopsy among patients with a strong suspicion of PCa. Using two independent query language databases, the PCMT addressed sampling errors related to prostate biopsy and gave more evidence concerning the clinical uncertainty surrounding an initial negative prostate biopsy [173]. Notably, the PCMT is not FDA approved.

#### **4. Discussion**

Globally, PCa is regarded as the most predominant malignancy in males and the principal cause of cancer-related mortality. Given the negative impact of PCa as it relates to morbidity and quality of life as well as mortality, early detection of the disease is of critical importance. Presently, tPSA and DRE are used in the diagnosis of PCa but there are a number of limitations which include low specificity and sensitivity leading to over-diagnosis and subsequent unnecessary prostate biopsies. In the early stage of PCa development there are no symptoms so there is a need for biochemical and molecular diagnostic tests for accurate and prompt detection. This review documents a number of PCa diagnostic and prognostics tests that have been discovered in the last two decades due to improvements in genomic technologies. Among them are serum PHI and 4K score, urine PCA3 and SelectMDx, and tumor tissue Oncotype DX, Decipher and Prolarix. These biomarkers are used in clinical decision making for PCa such as suspected patients who are required to undertake an initial prostate biopsy, or those who need a second biopsy given that the initial one

was negative for the disease. These tests have generated new prospects for advancing PCa diagnosis, prognosis such as the prediction of metastatic disease recurrence and decisions regarding therapy. In particular, Oncotype DX afford information regarding risk stratification which aid in identifying PCa patients that should receive treatment after a positive prostate biopsy, and Decipher those individuals to be treated post-surgery [174].

There have been a substantial number of miRNA-based assays and evidence in the literature demonstrates increased or decreased expression levels of miRNAs in peripheral blood (serum and plasma), prostate tissues and body fluids (urine and seminal) that differentiate between PCa and BPH patients, as well as healthy controls. This review presented findings on the clinical utility of miRNAs as diagnostic and prognostic biomarkers, and possible therapeutic targets for PCa. The findings are encouraging particularly the downregulation or upregulation of miRNAs expressions in plasma, serum or urine which facilitates the non-invasive nature, fast and cost-effectiveness of the tests. However, significant work is warranted if the miRNAs biomarkers are to translate from bench to routine clinical use. Also, larger observational prospective studies are needed with the intent of substantiating the validity of miRNAs and determining precise stratification based on the expression levels of miRNAs in PCa at different stages along the continuum of the disease. Furthermore, there are issues to be addressed such as the absence of guidelines for miRNAs development including isolation protocols, differences in study designs, pre-analytical variables such as specimen collection issues and tumor heterogeneity, need for validation and the selection of the best detection method (qRT-PCR vs. microarray) [175].

The presence of proteins and miRNAs in exosomes of prostate tissues makes them a valuable molecular diagnostic biomarker and therapeutic tool for PCa treatment. The development of these biomarkers is still in its early stages, but the results are very encouraging. The evidence presented in this review suggests that serum, plasma and urine exosomal miRNAs are useful for early detection as it differentiate metastatic from localized PCa together with prognosis of mCRPCa. There is a paucity of studies on exosomal proteins such as serum claudin 3 and survivin as well as urine-based LAMTOR1, TMEM256 and PARK7. This is an exciting area and research is continuing. However, there are challenges such as the complex process of obtaining appropriate samples, the lack of suitable isolation protocols and the need to standardized purification and quantification methods [176]. Overcoming these obstacles in preclinical research could result in these exosomal biomarkers being applied in the clinical setting for risk stratification, prognosis and the monitoring of PCa.

Moreover, PCa biomarkers employ diverse types of samples such as blood (serum or plasma), urine, prostate tissue (specimen from transurethral resection, biopsy and radical prostatectomy) and seminal fluid [177]. Assays comprising these PCa biomarkers are evaluated as their clinical use involved improving early diagnosis and risk stratification of localized tumor, reducing the number of needless biopsies with subsequent saving on use of expensive intervention strategies [177].

Traditionally blood, a minimally invasive and easily obtained sample is the chief source of PCa biomarkers for example serum tPSA and in recent years emerging biomarkers such miRNA has been developed. Panels for new PCa biomarkers will permit fingerprinting of the biologic behavior of the tumor with possibly personalized therapy and monitoring [178]. The collection of urine sample for the assessment of PCa biomarkers is a simple, non-invasive approach and assays can be used to monitor PCa with heterogeneous tumor foci [179]. Currently some of the emerging protein

#### *The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

and non-protein non-invasive urinary PCa biomarkers comprise TMPRSS2, PCA3, miRNAs and SelectMDx [180]. Genomic studies and biotechnological advancement have improved the sensitivity and specificity of these urinary PCa biomarkers thus enhanced clinical outcomes relating to early diagnosis and better selection of treatment approaches [181].

Tissue-based PCa biomarkers are invasive and fewer, and in the area of molecular diagnostics there is more focus on assays for blood and urine-based biomarkers with diagnostic and prognostic potential. However, there is growing interest on tissue-based PCa biomarkers such as ProMark, Oncotype DX and Decipher as well as miRNA and exosomal miRNA [182].

Finally, there are emerging molecular biomarkers that are at different phases of development, and many are in the preclinical phase. It is hope that in the next decade or so a significant collection of biomarkers with excellent diagnostic good sensitivity and specificity together with significant prognostic potential will be available for use by physicians in the clinical setting [183–185].

#### **5. Conclusion**

This review provides evidence of the use of established and emerging biomarkers detected in body fluids as diagnostic and prognostic tools for PCa. Despite the promising findings in preclinical and clinical research among the increasing body of investigations, there are challenges which delay the translation of a number of biomarkers from bench to bedside. Nonetheless, the considerable prospect of the biomarkers such as miRNAs and exosomal miRNAs in clinical practice as therapeutic tools for PCa is widely acknowledged and hopefully will be a reality in the not too distant future.

#### **Nothing to disclose**

The authors have no funding and conflicts of interest to disclose regarding the content of this chapter.

### **Author details**

Donovan McGrowder1 \*, Lennox Anderson-Jackson1 , Lowell Dilworth1 , Shada Mohansingh1 , Melisa Anderson Cross2 , Sophia Bryan3 , Fabian Miller4,5, Cameil Wilson-Clarke3 , Chukwuemeka Nwokocha3 , Ruby Alexander-Lindo3 and Shelly McFarlane6

1 Faculty of Medical Sciences, Department of Pathology, The University of the West Indies, Kingston, Jamaica

2 School of Allied Health and Wellness, College of Health Sciences, University of Technology, Kingston, Jamaica

3 Faculty of Medical Sciences, Department of Basic Medical Sciences, The University of the West Indies, Kingston, Jamaica

4 Faculty of Science and Technology, Department of Biotechnology, The University of the West Indies, Kingston, Jamaica

5 Faculty of Education, Department of Physical Education, The Mico University College, Kingston, Jamaica

6 Faculty of Medical Sciences, Caribbean Institute for Health Research, The University of the West Indies, Kingston, Jamaica

\*Address all correspondence to: dmcgrowd@yahoo.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*The Clinical Usefulness of Prostate Cancer Biomarkers: Current and Future Directions DOI: http://dx.doi.org/10.5772/intechopen.103172*

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## Section 2
