*2.3.1.5 Metabolomics*

The identification and analysis of metabolites, which are small-molecule intermediate products in metabolic reactions, is referred to as metabolomics. Because metabolites reflect both hereditary and environmental factors, a comprehensive metabolic examination is typically referred to as a "functional readout" of the current status of the organic system. The Human Metabolome Database (HMDB) is a freely accessible web resource that contains complete information on the human metabolome. The human metabolome is made up of peptides, lipids, amino acids, nucleic acids, carbohydrates, organic acids, biogenic amines, vitamins, minerals, and other tiny molecules found in the human body. The overall number of metabolites in HMDB 4.0 has increased dramatically from 40 153 in HMDB 3.0 to 114 100 in HMDB 4.0. This equates to approximately a fivefold increase [48].

In the context of PPM, metabolomic data could provide insight into an individual's unique physical reaction to a medicine, a technique known as metabolomics [49]. Currently, metabolomic studies of biofluids and tissues have aided in the development of PPM methods by discovering illness biomarkers that have the potential to aid doctors in diagnosis and early treatment. Because metabolites, unlike most proteins, travel throughout the body and appear in easily accessible biofluids like blood and urine one of the primary clinical advantages of metabolomics is that measurements may be conducted noninvasively [50]. Nuclear magnetic resonance (NMR) spectroscopy was commonly employed to identify metabolites in the early days of metabolomics research, but over the last decade, there has been a big movement toward MS, which offers superior resolution and sensitivity to small concentrations [51, 52].

Metabolite profiling of tissue and body fluid specimens with the purpose of biomarker discovery in OSCC research has revealed significant changes in energy metabolism pathways, according to mass spectrometry-based metabolomics analysis (eg, glycolysis and tricarboxylic acid cycle) [53]. Glycolytic metabolites (e.g., glucose) had higher amounts in OSCC patients' serum, while specific amino acids have lower levels (ie, valine, tyrosine, serine, and methionine) [54]. In OSCC tumour tissues, however, similar metabolite expression patterns are reversed, implying that this signature panel could be used as a screening tool. Early research, on the other hand, must be backed up with well-designed tests. Lipids have also been discovered as a significant class of metabolites, and abnormal cholesterol levels in the blood have been associated to a variety of cancers [55–57]. Total lipids, cholesterol, and high-density lipoprotein levels were shown to be considerably lower in OSCC patients compared to healthy controls [58, 59]. These lipidomic observations, albeit preliminary, may indicate greater usage of novel membrane synthesis by neoplastic cells and require further exploration.

These technologies will be collectively strong, with the potential to disclose molecular mechanisms and critical signalling pathways driving the disease, thanks to the rapid development of the omics tools outlined above. Furthermore, these tools have the potential to be utilised to identify new therapeutic targets as well as biomarkers that can be used to diagnose disease Cancer diagnosis, prognosis prediction, and treatment surveillance could all benefit from this technology.

### *2.3.1.6 Companion diagnostics (CDx)*

The US FDA produced the first regulatory guideline document on CDx in 2014, and it was here that this type of assay was officially defined for the first time [60].

A CDx test is an in vitro diagnostic equipment that delivers information necessary for the safe and effective use of a related therapeutic product, according to this definition. This means that testing with this sort of assay is required and must be disclosed in both the medication and CDx assay labelling. CDx devices help clinicians provide the most effective, tailored medicines for their patients. In specific sections of DNA, relevant genetic information for defining malignancies can be identified (i.e., oncogenes). Some CDx are based on these specific oncogenes and can be used to evaluate whether or not a person will respond to a certain treatment in order to avoid sequencing the entire genome and obtaining superfluous information. Each CDx is linked to a certain medicinal treatment, which is linked to a particular genetic defect for which it is most effective [61]. Within the category of CDx products, there are a range of diagnostic procedures, each with its own role. Immunohistochemistry (IHC), fluorescent in situ hybridization (FISH), and realtime quantitative PCR (RT-qPCR) are examples of these techniques [62].

#### *2.3.1.7 Data storage*

After collecting the OMICS data, storage and analysis also poses a great challenge. The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA; sponsored by the National Cancer Institute and the National Human Genome Research Institute) are two large databases for oncology data. The data gateway of the International Cancer Genome Centre (ICGC) focuses on 50 tumour types and defines them on genomic, transcriptomic, and epigenomic levels across genders, mutations, tumour stage, and other factors. The TCGA portal has thorough information on genetic alterations and gene expression in 11 different types of cancer tissues and 33 different cancer subtypes. On a large number of patients, analysis is performed on high-quality tumour samples and matched normal tissue samples [63, 64].

#### **2.4 Single nucleotide polymorphisms (SNPs)**

Despite the lifestyle habits of exposure to high risk factors for oral cancer with 80% attributable risk of tobacco per se, a small proportion of the tobacco habit develop persistent premalignant lesions, and 3–8% transform to the malignant phenotype. Genomic variants, somatic mutations and epigenetic regulation play a critical role in oral cancer. SNPs are the most common genomic variants. SNPs are single base changes in a gene's exonic coding region or non-coding intronic regions that affect gene expression and function directly or indirectly in more than 1% of an ethnic population. SNPs in intronic regions may modify the three-dimensional structure of DNA, causing changes in molecular attributes such as Gibbs free energy aectLnJ stability, as well as impacting DNA polymerase activity and transcription factor activity. Binding SNPs can be found in one or both alleles, giving rise to heterozygous or homozygous genotypes. The wild-type (WT) allele is the ancestral allele, while the SNP allele is the changed allele. In a cancer case–control group, the frequency of allelotypes and genotypes differed, indicating a link between SNPs and cancer [65].

The connection of SNPs with risk propensity or susceptibility to oral cancer has been studied in several populations. In a meta-analysis research, the authors analysed SNPs in oral cancer and identified 34 SNPs in 30 genes that are strongly linked with oral cancer [66]. SNP rs1800471 in TGF- gene, with GC genotype associated with increased risk and GG genotype with lower risk in numerous studies and populations; SNP rs1048943 in CYP1A1 gene, with AG + GG genotypes resulting in increased risk and WT AA genotype resulting in decreased risk. The GSTM1

*Personalised Precision Medicine - A Novel Approach for Oral Cancer Management DOI: http://dx.doi.org/10.5772/intechopen.99558*

null genotype was linked to an elevated risk, while the WT genotype was linked to a lower risk. Similarly, heterozygous genotypes of SNPs rs1800870-AG in the IL-10 gene, rs11549467-GA in the HIF gene, and rs861539-CT in the XRCC3 genes were linked to an increased risk of oral cancer, while WT genotypes were linked to a lower risk. The WT genotypes rs1801133-CC in MTHFR and rs20417-GG in COX-2, on the other hand, were related with an increased risk, while the corresponding SNP homozygous genotypes TT and CC were associated with a decreased risk [67].

SNP analysis using high-throughput genomic analysis, as reported in genomewide association studies (GWAS), and next-generation sequencing has emerged as a strong tool for identifying susceptibility loci, allowing information on thousands of SNPs to be obtained at the same time. These platforms often work with smaller samples and are more expensive, thus they must be confirmed in larger samples using alternative technology such as nucleotide sequencing and real-time PCR.SNPs investigated in various studies contribute to increased susceptibility to oral cancer, and a panel of SNPs could be used as Predictive Biomarkers to screen high-risk individuals who are prone to oral cancer due to tobacco use, providing an objective, unbiased test assay to assess oral cancer risk in individuals [66].

#### **2.5 Developing a PPM therapy**

#### *2.5.1 The application of omics data to treatment*

Establishing the link between biological data, disease, and clinical translation is a fundamental difficulty in PPM: how can we understand the data collected to make meaningful medical decisions? In the medical field, "Big Data" refers to a larger collection of medical data encompassing the tracking of various medical indicators and biomarkers across thousands of individuals (primarily clinical and omics data). Researchers may test tissues for thousands of molecular targets using high-throughput data gathering, effectively recording the response of a complex system over time.

The reconciliation of various omics components allows for the generation of prediction models of human physiology that may be employed in experimental design and clinical trial development in the field of systems biology [68].

Systems biologists and bioinformatic scientists use statistically significant trend detection approaches to link observations to biological events and phenotypes. Multivariate decomposition techniques, predictive modelling and optimization strategies, and other statistics-based tools are examples of these. Statistically understanding Big Data trends is a separate field that is required for predictive modelling, clinical decision-making, and assistance [69].

Drug discovery techniques for a number of PPM cancer products have been developed thanks to advances in omics technologies. Circulating tumour cells (CTC) and DNA detection approaches have promise not just for early diagnosis, but also for personalised patient risk monitoring and the development of effective personalised therapy. Several other cancer medicines in development take advantage of the immune system's particular power and specialisation to fight cancer. This has been the focus of research for almost a century, and it has evolved into a distinct discipline known as immunoengineering. The ultimate goal of this profession is to tailor a more particular and potent immune response, which can lead to a powerful and effective personalised cancer treatment [70].

#### *2.5.2 Early cancer detection using CTCs and DNA*

CTCs and circulating tumour DNA (ctDNA), two forms of oncological biomarkers, have emerged as the face of non-invasive cancer diagnosis using "liquid biopsy" procedures. Because research has shown that tumours release both types of biomarkers into the circulation early in cancer progression, there has been a lot of focus on their use in early detection and screening. CTCs and ctDNA are likely to become more effective in risk stratification, illness monitoring, and tailored treatment selection as research progresses and technology improves [71].

230 OSCC patients at various pathological stages of the disease and treatment modes were enrolled in a cross-sectional observational study. CTCs were obtained utilising the Onco Discover liquid biopsy method, which is based on immunomagnetic CTC enumeration and has been authorised by the Drug Controller General of India. The presence of CK18 and well-defined, DAPI-stained nuclei were found in CTCs. CTC were counted and then examined for stage, extracapsular spread (ECS), lymphovascular emboli (LVE), perineural invasion (PNI), and depth of invasion, among other clinicopathological criteria (DOI). To distinguish between early and advanced stages of dialysis, CTC cut off values were obtained. CTCs in OSCC patients were found to be associated with cancer stages (clinical and pathological) and aggressive pathological characteristics. We saw a 25–50 percent increase in CTC number when aggressive clinical characteristics were present, which frequently indicate a bad prognosis. Treatment-naive patients had a reduced number of CTCs in the early stages. The number of CTCs in advanced-stage OSCC patients was 50% greater than in early-stage OSCC patients. CTC might be considered a trustworthy measure to predict the disease outcome in oral cancer due to a positive connection of CTC number with numerous pathophysiological characteristics. The presence of CTC at all stages of the disease shows that OSCC is most likely a biologically systematic disease [72].
