**3.1 Metabonomics**

Metabonomics is a comprehensive assessment of low-molecular-weight (< 1000 Da) endogenous metabolites, which can reflect biochemical reactions and metabolic changes under given physiological or pathophysiological conditions. Endogenous metabolites include a variety of small molecules such as sugars, lipids, steroids, and amino acids. The expression of these metabolites in humans represents the functional phenotype of cell, organ, or tissue. Metabonomics is of great help in identifying disease-related metabolites. Through the detection of various biological fluids (blood, urine, bronchoalveolar lavage fluid), it can provide help for the early detection of complex diseases and in-depth understanding of the pathogenesis of diseases. Smoking increases levels of nicotine and its metabolites, but also has a strong effect on the systemic metabolism of amino acids, lipids, and other small molecules. A study that recruited 211 subjects with COPD found that peripheral blood monocyte sphingolipid pathway enzyme expression and plasma small molecules such as ceramide were biomarkers of COPD and emphysema, even after adjustment for smoking. Subsequent targeted plasma metabolomics studies in 129 subjects with COPD genes further identified five sphingomyelins associated with emphysema and four trihexosylceramides and three dihexosylceramides associated with COPD exacerbations [11]. These findings support sphingolipids as potential new therapeutic targets for emphysema. Urine is also a common and available sample for metabolomics studies. Urine metabolomics is less invasive to participants than serum/ plasma because serum metabolites remain relatively constant due to the balance of metabolism in the body, and urine samples are more suitable for metabolomics differential analysis than blood. Urine profiles of COPD patients and healthy controls were successfully isolated with ultrahigh performance liquid chromatography/MS (UPLC/MS)-based metabolomics. Ten metabolic biomarkers associated with COPD were identified in urine samples involving amino acid metabolism, lipid and fatty acid metabolism, and glucose metabolism. Amino acid metabolism is related to nutritional status, oxidative stress, and inflammatory response. Muscle dysfunction is an important feature of COPD patients, particularly during cachexia. In COPD patients, the concentration of histidine is increased, and muscle is synthesized by methyltransferase conversion to methylhistidine during cross-linking of actin and inosine [12]. Reduced use of histidine for muscle growth may result in increased serum histidine levels. In COPD patients, branched-chain amino acids (BCAAs) have been reduced; BCAAs regulate protein production and glucose homeostasis by continuously delivering BCAAs to skeletal muscle [13]. The reduction of BCAAs in COPD may indicate the risk of protein malnutrition. For underweight COPD patients, hypermetabolism caused by COPD exacerbation and respiratory muscle weakness is the main reason for the decline of BCAA concentration. However, the hydrolysis of muscle proteins and the consumption of branched-chain amino acids are part of the basic physiological function of providing carbon for gluconeogenesis during fasting. Cachexia patients with weight loss show increased gluconeogenesis [14], which will lead to increased consumption and decreased content of BCAAs in humans.

#### **3.2 Proteomics**

Proteomics aims to identify potential protein biomarkers of disease and has become a popular tool for both basic and clinical research. Proteomics has the potential to reveal some disease mechanisms that cannot be determined at the genomic level and has the great advantage of direct clinical relevance. Proteomic approaches have been used in many chronic lung diseases, such as cystic fibrosis, idiopathic pulmonary fibrosis (IPF), sarcoidosis, asthma, and so on. With the development of analysis and detection technology, the identification of potential protein biomarkers can be achieved in COPD research. Plasma proteins are involved in inflammation, coagulation regulation, lipid metabolism, and oxidative stress, and changes between healthy people and mild COPD can be evaluated at an early stage of the disease, helping us to identify early COPD. Currently, the most promising blood biomarker for COPD is sRAGE. sRAGE is an isoform of the advanced glycation end product (RAGE) transmembrane receptor that lacks a transmembrane domain through proteolytic cleavage. RAGE is encoded by the AGER gene, and SNPs in AGER have been associated with COPD and emphysema in targeted and genome-wide association studies [15]. RAGE binds damage-associated molecular pattern molecules to perpetuate inflammation in lung epithelial cells. In COPDGene, subjects with more severe emphysema had lower plasma sRAGE. The SNP in AGER (rs2070600) was associated with lower sRAGE plasma levels in COPDGene and other cohorts. Plasma sRAGE is a predictor of emphysema progressions, and it will be the first blood biomarker for emphysema to be submitted to the US Food and Drug Administration and the European Medicines Agency Biomarker Certification Program [16, 17]. While sRAGE is currently the best biomarker for emphysema, blood markers of inflammation are also associated with COPD severity and progression. In a study of 2123 subjects from COPDgene and 1117 subjects from SPIROMICS, plasma IL-6 and IL-8 were found to be positively associated with emphysema progression, but not with COPD severity and smoking status [18]. The detection of proteins in BALF and EBC can also help to clarify the pathogenesis of COPD and lung defense mechanism. In order to obtain an accurate diagnosis of COPD, an invasive approach is required in some cases. Lung tissue sample obtained by transbronchial lung biopsy or open lung biopsy procedure can also be used to analyze proteomic changes. Comparisons of lung tissues from COPD patients and healthy controls using MALDI-TOF-MS revealed significantly higher levels of matrix ferroproteinase-13 (MMP-13) and thioredoxin-like 2 in COPD patients, which may be more closely associated with the development of airflow limitation. In COPD patients, the level of SP-a in lung tissue was increased, and the level of SP-a in induced sputum supernatant was increased, but the levels of other surfactant proteins (SP-B, SPC, SP-D) were not changed. These results suggest that SP-a may be involved in the pathogenesis of COPD. However, the determination of a protein as a biomarker requires a large amount of sample data as a basis. We still have a lot of work to do.

#### **3.3 Transcriptomics**

The aim of transcriptome analysis is to capture coding and non-coding RNAs and quantify the heterogeneity of gene expression in cells, tissues, organs, and even the whole body. Transcriptomics can provide functional characterization and annotation of genes/genomes previously revealed by DNA sequencing [19]. Currently, three transcriptomics-related technologies are employed, including real-time quantitative PCR (qPCR), microarray, and RNA sequencing. The sample sources for the COPD

#### *Exploration of Multi-Aspect Development of Chronic Obstructive Pulmonary Disease… DOI: http://dx.doi.org/10.5772/intechopen.106643*

transcriptomics study were focused on peripheral blood, lung tissue, and sputum. Similar to the epigenome, the transcriptome can be influenced by factors such as age, gender, cell type, environmental exposure, and disease status. For example, a study conducted microarray gene expression profiling of peripheral blood mononuclear cells collected from 136 COPDGene subjects and found that 1090 transcripts associated with FEV 1% prediction and 1745 transcripts associated with FEV 1/FVC, genes that overrepresent pathways associated with immunity, inflammatory response, and sphingolipid (ceramide) metabolism and signaling. At single cell level, COPD was found to be associated with a decreased ratio of specific transcriptome features of CD4+ resting memory cells and naive B cells [20]. There are also studies using gene expression profiling of lung tissue to explore the molecular pathogenesis of early COPD with emphysema. RNASeq was used to detect 16,676 genes expressed in lung tissue. Among them, 1226 genes in the COPD group with emphysema and 434 genes in the non-emphysema group were differentially expressed with healthy smokers [21]. Xiao et al. explored the relationship between gene transcriptomics and several single-nucleotide polymorphisms in sputum. Distal gene loci and biomarker encoding genes may influence circulating levels of COPD-associated pneumonia proteins, and a subset of these protein quantitative trait loci may influence their susceptibility in the lung and/or COPD. A notable feature of transcriptomics research is that the number of potential transcription variables is usually very large, and special methods are needed to deal with the huge and disordered data. For example, the weighted gene coexpression network and clustering method can be used to reduce the dimensionality.

COPD exacerbations are highly heterogeneous events associated with increased airway and systemic inflammation and physiologic changes, and reliable and objective biomarkers are invaluable to aid diagnosis and guide appropriate treatment. In blood, urine, breath samples (including exhaled breath, sputum, bronchoalveolar lavage fluid, and bronchial biopsies), levels of various immune inflammatory cells and molecules are elevated, such as CRP, PCT, BNP, plasma fibrinogen, IL-6, sputum eosinophilia, IL1β, CXCL10 (IP-10), some of which have been used in clinical examinations to assist in the evaluation of COPD deterioration [22]. At present, the research on the pathogenesis of AECOPD is still insufficient, and there are contradictory conclusions. In recent years, the widespread use of high-throughput sequencing technology has enabled us to study COPD in greater depth. At the metabolomics level, newly discovered markers of differential metabolism may be associated with disease states; at the proteomics level, several disease-related proteins have been identified and are expected to be used in the early diagnosis of COPD, while in transcriptomics, some biomarkers may be used to evaluate the prognosis of the disease. In general, multi-omics studies provide a way to discover biomarkers for early diagnosis of COPD, but the identified prospective biomarkers need to be clinically validated for early diagnosis of COPD. Therefore, clinicians need to collect a large number of patient data and clinical samples. Only by combining proteomics, transcriptomics, metabolomics, and bioinformatics, can we obtain reliable and helpful results for clinical diagnosis and treatment.
