3.1 Introduction

Cancer, a heterogeneous group of diseases, represents the second leading cause of death globally and is responsible for an estimated ten million deaths worldwide in 2020 (Sung et al. 2021). For advancement and better treatment in cancer, patient’s early diagnosis and accurate detection of cancer are the crucial steps. In the past few decades, the biomarker field along with the improved quality of medical services and technologies has transformed the ability of cancer researchers to easily diagnose and classify cancer at the molecular level and resulted in improved drug development and clinical trial design (Hu and Dignam 2019; Parker et al. 2021; Goossens et al. 2015; Goyal et al. 2021). The definition of biomarker has been given differently by different groups. Some of the definitions limit the scope of biomarkers up to biological molecule or biochemical features; on the other hand, a broader definition of biomarker provided by the Biomarker Consortium (Foundation of National Institute of Health) increases the probability of discovering new biomarkers in the ever-changing era of research biology (Califf 2018; Wu and Qu 2015; Strimbu and Tavel 2010). According to United Nations, World Health Organization (WHO) biomarkers can be defined as any measurable substance, structure, or process or its product that can predict the incidence of disease outcome (World Health Organization 2001). In simple words, any measurable indicator of disease condition and treatment response indicator can be considered as a biomarker (Zare Jeddi et al. 2021; Lassere 2008). In a study in 1848, light chain of immunoglobulin was identified as the first-ever cancer biomarker in the myeloma patient urine sample (Solomon 1980). Since then, numerous cancer biomarkers have been identified such as alfa-fetoprotein, carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). Every era of cancer biomarker discovery has been closely associated with the new and powerful technology (Tatarinov 1964; Xu et al. 2021; Campos-da-Paz et al. 2018; Cózar et al. 2021; Wang et al. 1979).

In the past few decades, high-throughput technologies such as genomics, metagenomics, transcriptomics, proteomics, and metabolomics have generated a significant amount of data for cancer biology. For example, a proteomics-based study identified DNA-dependent protein kinase catalytic subunit (DNA-PKcs) as a potential diagnostic biomarker for clinical outcomes in breast cancer (Asleh et al. 2021). A combined study of transcriptomics with metabolomics revealed a panel of important serum biomarkers such as 2-hydroxybutyric acid and 4-hydroxybutyric acid as candidate diagnostic biomarkers for lung cancer proliferation through the Ca2+ signaling pathway (Zheng et al. 2021). In addition, miRNA-194 was identified as a favorable prognostic biomarker for gastric cancer (Wang et al. 2021a). Moreover, the machine learning-based computational algorithm helps to classify the complex pattern of cancer research outcomes generated by a plethora of high-throughput experiments (Echle et al. 2021). As a result, these omics approaches when coupled with bioinformatics and computational methods provide great opportunities for biomarker discovery and facilitate therapeutic development for cancer (Menyhárt and Győrffy 2021; Yan et al. 2016).

As with time, the information about cancer biomarkers has expanded, and so also the complexities of tumor biology evolve adding challenges to the development of efficient biomarkers. More powerful tools, cross-validation techniques, and system biology approaches should be deployed in future to increase the yield of biomarkers in cancer therapeutics (Ileana Dumbrava et al. 2018; Sheng et al. 2020; Louie et al. 2021). In this chapter, we have highlighted three aspects of cancer biomarkers. The first part deals with the cancer biomarker classification according to the molecular types and the potential role they play in clinical oncology. The second part relates to the omics approaches that are nowadays considered as a powerful strategy to untangle the complex biological behavior of cancer cells. These high-throughput technologies help to discover molecular biomarkers with prognostic, diagnostic, and targeted therapeutic values. The third part discusses some important bioinformatics tools and software related to cancer biomarker discovery. The multiomics data integration and molecular regulatory network-based analysis tool can revolutionize the biomarker field for the effective treatment of cancer.

3.2 Categorization of Cancer Biomarkers

In biomedical science, cancer biomarker identification is one of the major multidisciplinary areas and their categorization should be considered contextual. Further, due to the enhancement in recent advanced technologies, enormous cancer biomarkers have been reported thereby it is difficult to categorize cancer biomarkers by considering only one aspect (Henry and Hayes 2012). However, according to contemporary findings, several attempts have been applied to classify cancer biomarkers (Nguyen et al. 2020). A basic schematic representation for the classification of cancer biomarkers is shown in Fig. 3.1.

Fig. 3.1
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Classification of cancer biomarkers based on biomolecules, clinical utility, and other criteria

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3.2.1 Classification of Cancer Biomarkers Based on Biomolecules

3.2.1.1 DNA Cancer Biomarkers

Genetic alteration, gene rearrangement, and point mutation are responsible for cancer progression (Housman et al. 2014; Torgovnick and Schumacher 2015; Li et al. 2021a). The most common cancer DNA biomarker includes single nucleotide polymorphisms (SNPs). In a recent study, SNP–SNP interactions have been identified as a potential indicator of prostate cancer aggressiveness (Lin et al. 2021). Another study demonstrated m6A-associated functional SNPs (PLEKHA8, SMUG1, CDC123, RMI2, ACSM5) as major functional variants for thyroid cancer (Ruan et al. 2021). Moreover, researchers have defined circulating tumor DNA in localized nonsmall cell lung cancer as a prognostic biomarker and predicted the survival rate (Peng et al. 2020). Epigenetic CpG methylation also provides a broad range for early cancer detection and can be used as a cancer biomarker (Locke et al. 2019).

3.2.1.2 RNA Cancer Biomarkers

Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR), microarray, Serial Analysis of Gene Expression (SAGE), differential display, microfluid card, and bead-based methods are commonly used to detect RNA or miRNA cancer biomarkers (Xi et al. 2017; Yamashita et al. 2008). Various identified mRNA and miRNA act as effective diagnostic and prognostic cancer biomarkers (Paramasivam 2021; Wang et al. 2021b; Bautista-Sánchez et al. 2020). In blood-based liquid biopsies, platelet RNA has been detected as an early diagnostic biomarker for cancer (Wurdinger et al. 2020). A recent article has indicated that miRNA plays an important role in identification of drug and decision-making of drug delivery in cancer therapeutics (Paramasivam 2021). Clinical researches reported that circRNAs expression indicates the cancer prognosis in different types of cancer, for example in a recent study circRNA expression in peripheral blood has been found to be correlated with cancer size (Li and Han 2019; Pan et al. 2019; Chen et al. 2017).

3.2.1.3 Protein Cancer Biomarkers

The most commonly used techniques used to identify protein-based biomarkers include Polyacrylamide Gel Electrophoresis (PAGE) and Two-Dimensional fluorescence Difference Gel Electrophoresis (2D-DIGE) (Issaq and Veenstra 2008). The high-throughput method includes proteomics study based on Mass Spectroscopy (MS), Surface-Enhanced Laser Absorption Desorption Ionization Time of Flight (SELDI-TOF), and Matrix-Associated Laser Absorption Desorption Ionization Time of Flight (MALDI-TOF) and discovered various protein and peptide biomarkers for ovarian and breast cancer (Zeidan et al. 2009; Liu 2011; Swiatly et al. 2017; Lv et al. 2019). Quantitative proteomics has also been applied to identify potential cancer protein biomarkers in different types of cancers (Kwon et al. 2021). To discover secreted protein-mediated interaction between the cancer cell and nonmalignant stroma, Stable Isotope Labeling with Amino Acids in Cell culture (SILAC) was performed and further in pancreatic cancer, Wntless homolog protein (WLS) and Myristoylated Alanine-rich C-Kinase Substrate (MARCKS) found to be associated with oxaliplatin resistance (Kim et al. 2021; Wang et al. 2018). Isobaric Tags for Relative and Absolute Quantitation (iTRAQ), a quantitative proteomics approach was applied to find differentially expressed protein in metformin-treated cervical cancer cells and reported that metformin increases tumor suppressor gene expression IGFBP7 (Xia et al. 2020). Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) and antibody arrays are used in lung breast and colon cancer to find a panel of potential protein biomarkers (Wang et al. 2016a; Huang and Zhu 2017). Protein-based biomarkers are considered as a more valuable biomarker as compared to DNA- and RNA-based biomarkers as they are involved in functional molecular pathways and determine the disease initiation and progression state (Zhang et al. 2019). In a recent study, PINK1 protein has been reported as a prognostic biomarker for cancer (Zhu et al. 2020).

3.2.1.4 Carbohydrate Cancer Biomarkers

Cancer progression is often associated with changes in the expression of surface carbohydrates such as N-linked and O-linked glycans (Leney et al. 2017). The glycobiomarkers [glycoprotein, glycolipid, and proteoglycan] serve as candidate epidemiological cancer biomarkers (Lan et al. 2016; Daniotti et al. 2013). Mass spectrometry such as MALDI-TOF and Electrospray Ionization (ESI) are generally used for profiling of N- and O-linked glycosylation at serine and threonine residue of candidate protein molecules in human sera and cancer cell lines (Dube and Bertozzi 2005; An et al. 2006, 2010; Drake et al. 2017). Glycan microarray analysis has been found a good biomarker identification method for the diagnosis of breast cancer (Wang et al. 2008). Further in hepatocellular carcinoma, cancer-associated carbohydrate antigens (DSGG, fucosyl GM1, and Gb2 of CACAs) have been reported as potential biomarkers for early detection of cancer (Wu et al. 2012).

3.2.2 Classification of Cancer Biomarkers Based on Clinical Utility

Based on the putative application, cancer biomarkers can be classified under the following categories. Although some biomarkers are overlapping in nature, for example, the grading and staging cancer biomarker is also used as a prediction and screening biomarker (Ludwig and Weinstein 2005).

3.2.2.1 Prediction Cancer Biomarker

Predictive biomarkers predict the response and efficacy of the treatment and also help to determine the optimal dose of the drug at the initial treatment stages (Alves Martins et al. 2019; Bai et al. 2020). As cancer is a heterogeneous disease and the same cancer type responds differently to a drug thereby these types of biomarkers help in selecting a successful treatment process and minimizing the drug toxicity. A common predictive biomarker is overexpression of HER2, which predicts breast cancer’s response to drugs like trastuzumab (Jørgensen and Hersom 2016). A high level of circulating IFN-γ predicts the response of immunotherapies to immune checkpoint blockade in melanoma and nonsmall cell lung cancer patients (Karachaliou et al. 2018). A more recent study predicts that overexpression of excision repairs cross-complementation group 1 (ERCC1) increases DNA excision repair and imparts resistance to platinum-based drugs (Chung 2021). Additionally, in colorectal cancer, the mutation in MAPK pathway genes serves as a predictive biomarker for EGFR therapy and indicates resistance to cetuximab drug (Boussios et al. 2019).

3.2.2.2 Detection/Diagnostic Cancer Biomarker

The screening or detection of cancer biomarkers is the real indicator of the presence of cancer. These biomarkers help to identify benign cancer before metastasis. The tumor cells produce several immune factors, serum proteins, and circulating free DNA and RNA and these molecules can serve as cancer detection biomarkers (Parker et al. 2018). Diagnostic biomarkers play an important role in classifying patients into subtypes and also detecting the presence of the disease. Prostate-specific antigen (PSA) is the best-known cancer biomarker for prostate cancer early detection (Welch and Albertsen 2009). Cancer Antigen 19-9 (CA-19-9) is a diagnostic serum biomarker for pancreatic ductal carcinoma (Poruk et al. 2013). Further, another cancer antigen CA 125 is also used as a classical biomarker for the detection of ovarian cancer (Felder et al. 2014). The utility of cytokines as diagnostic biomarkers is increasing rapidly although further validation is required. IL-6 and VEGF serve as possible diagnostic biomarkers for ovarian and gastric cancer (Liang et al. 2015; Monastero and Pentyala 2017). Diagnostic biomarkers are often used in conjunction with other specific biomarkers to increase the specificity and diagnosis in the general population (Califf 2018).

3.2.2.3 Prognostic Cancer Biomarkers

Prognostic biomarkers allow to monitor the disease status, detect the recurrence rate, and provide an idea about the overall patient survival rate, independent of therapy (Sechidis et al. 2018; Ruberg and Shen 2015). It allows estimating the risk of the disease. In colon, lung, and breast cancer, patient’s carcinoembryonic antigen (CEA) signifies a poor survival rate (Boonpipattanapong and Chewatanakornkul 2006; Su et al. 2012). Some diagnostic biomarkers such as Cancer antigen 19-9 (CA-19-9) and cancer antigen (CA 125) have also prognostic values and their presence can predict the survival rate in pancreatic ductal carcinoma and ovarian cancer, respectively (Poruk et al. 2013; Felder et al. 2014). Other prognostic biomarkers include miR-155 which suggests a poor clinical prognosis in hepatocellular carcinoma (Nalejska et al. 2014). A recent study reported five signature miRNAs having prognostic values in colon cancer (Lv et al. 2020). Further for breast cancer, circulating tumor cells serve as a prognostic indicator in nonmetastatic breast cancer as their presence is correlated with metastasis (Lucci et al. 2012).

3.2.2.4 Pharmacodynamics Cancer Biomarkers

This type of biomarkers is the new classification-based biomarker that determines the degree of the drug response (Sarker and Workman 2007). Pharmacodynamic biomarkers provide an idea about the interaction between a drug and its suspected target and whether the drug exerted a cellular response or not, and thereby guide treatment decision-making plans in real-time (Jackson 2012). For example, in nonsmall cell, lung cancer patient measurement of Mitogen-Activated Protein Kinase (MAPK) pathway inhibition receiving BRAF inhibitors can suggest a direct interaction between drug and target genes (Gainor et al. 2014). Further, Ki67 acts as a biomarker for cell proliferation, its expression after treatment with endocrine therapy serves as a pharmacodynamic response, and indicates target drug effects (Kelloff et al. 2005). Another example of pharmacodynamic biomarker example is monitoring the activity of PARP enzyme in white blood cells for the development of anticancer drug Olaparib (Dick et al. 2021).

3.2.3 Classification of Cancer Biomarkers Based on Other Criteria

3.2.3.1 Imaging Cancer Biomarkers

X-ray, Positron Emission Tomography (PET), Computed Tomography (CT), ultrasound, radionuclide imaging, and Magnetic Resonance Imaging (MRI) are the imaging techniques that are routinely used in clinical oncology for diagnosis, screening, and staging of cancer (Dregely et al. 2018; O’Connor et al. 2017). In oncology, imaging biomarkers are cost-effective noninvasive tools that easily allow identifying the disease state including assessment of the drug response. Several attempts have been made to do a regular assessment to reduce the risk of cancer development. For example, colonoscopy and mammography have been found to reduce the risk of developing colon cancer and breast cancer, respectively (Bischoff 2014).

3.2.3.2 Pathological Cancer Biomarkers

Various types of infectious agents such as viruses and bacteria constitute 15–20% of all human cancers (Srivastava et al. 2005; McLaughlin-Drubin and Munger 2008). The presence of pathogenic agents within the tumor cell makes them attractive pathogenic cancer biomarkers. The presence of HPV is associated with cervical cancer (Burd 2003). Further, Epstein Bair Virus (EBV) is closely associated with lymphoma and nasopharyngeal carcinoma (Pagano 1999). Helicobacter pylori is an established biomarker for gastric cancer (Wroblewski et al. 2010). Besides, various cancer pathogen detection methods have been revolutionized recently for rapid biomarker detection in the complex biological sample including Bioluminescence Resonance Energy Transfer (BRET), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based biosensors, and ELISA (Wu and Qu 2015).

3.3 Omics Approaches in Cancer Biomarker Research

“Omics” studies have been characterized by high-throughput technologies which help to investigate the genome, transcriptome, epigenome, proteome, and metabolome of cancer cells. These omics approaches facilitate the understanding of carcinogenesis at the molecular level. Figure 3.2 depicts a schematic representation of omics approaches that are commonly used to study the cellular behavior of cancer cells and some important biomarkers identified by using these omics approaches.

Fig. 3.2
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Schematic representation of different omics approaches used for cancer biomarker discovery

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3.3.1 Genomics for Cancer Biomarkers

In oncology, numerous technologies such as Next-Generation Sequencing (NGS), Whole-Genome Sequencing (WGS), Comparative Genome Hybridization (CGH), and Fluorescence in situ Hybridization (FISH) have been widely used to analyze cancer-specific mutational changes (Hu et al. 2018; Zhao et al. 2019). Additionally, genomic studies primarily focus on the analysis of copy number variation and identification of chromosomal abnormality to characterize cancer cells at the molecular endpoint (Nogrady 2020). Further, the advances in sequencing technology helped the decision-making for personalized treatment strategy instead of based on cancer type. For example, FDA-approved biomarker EGFR mutation signifies the effectiveness of EGFR inhibitors like gefitinib (Tsimberidou et al. 2020). BRCA1 and BRCA2 mutations have been identified as hereditary markers for breast and ovarian cancer syndrome (Narod and Salmena 2011; Savanevich et al. 2021). Moreover, genomic profiling of pancreatic cancer identified centromere protein F as a novel therapeutic target and proved to be responsible for cancer progression (Chen et al. 2021).

3.3.2 Transcriptomics for Cancer Biomarkers

Transcriptomics studies are engaged in quantification, detection, and identification of altered mRNA, miRNA, and lncRNA in cancer cell populations (Chakraborty et al. 2018). Tools like RNA-seq, and microarray are commonly used to study the transcriptome. Expression Quantitative Trait Loci (eQTL) is a new approach for the analysis of functional variation sequence that leads to changes in gene expression (Hong et al. 2020; Geeleher et al. 2018). Various prognostic and predictive gene signatures have been identified in lung, breast, colon, and other tumor types (Vishnubalaji et al. 2019; Xiong et al. 2020; Sheng et al. 2019; Fang et al. 2021; Li et al. 2019). The transcriptomic study-based microarray technology has also been applied in precision oncology trials and aids in the clinical classification of breast cancer, colon cancer, and gastric cancer (Salem et al. 2017; Guinney et al. 2015; Lin et al. 2015). In breast cancer, transcriptomic data with bioinformatics study reveal BRIP1 as a noteworthy prognostic biomarker and its expression found to be correlated with various clinical features of breast cancer (Khan and Khan 2021). Furthermore, from RNA-seq data, a differential gene expression pattern has been revealed for different cancer tissue and their normal counterpart which will uncover the complex molecular pattern of cancer cells (Li et al. 2017). Different types of RNA serve as independent cancer biomarkers for instance in nonsmall cell lung cancer (NSCLC), the expression profiling of snoRNAs serves as an early diagnostic cancer biomarker (Liao et al. 2010). For renal cancer, hepatic cancer and glioblastoma piRNAs may serve as diagnostic and prognostic biomarkers (Busch et al. 2015; Liu et al. 2019; Rizzo et al. 2016). In addition, lncRNAs, such as XIST reported as a potential candidate biomarker for gastric cancer through transcriptomic study (Lu et al. 2017).

3.3.3 Proteomics for Cancer Biomarkers

Proteomic-based studies are considered as one of the innovative and dynamic high-throughput technology for determining the cellular function and location of main mediators of proteins (Olivier et al. 2019). The proteome of a defined entity be it a cell, an organelle, a tissue allows for better biomarker identification and to better understand the cancer surveillance mechanisms (Sallam 2015). Several studies reported that the proteomics approach can be used to identify the drug resistance nature of cancer cells and treatment resistance biomarkers; for example through mass spectrometry, PYCR1 and ALDH18A1 expressions have been identified to be significantly associated with drug resistance in breast cancer (Shenoy et al. 2020). The drug resistance of cancer is associated with stemness and by applying the proteomics approach, new specific cancer biomarkers and therapeutic targets have been identified in the breast cancer stem cell population (Koh et al. 2020). Protein profiling of patients receiving immunotherapy is necessary to monitor the therapeutic response and thereby proteomic study can help to discover the potential prognostic biomarkers for cancer therapeutics (Chae et al. 2020; Harel et al. 2019).

3.3.4 Metabolomics for Cancer Biomarkers

Cancer affects intracellular metabolism and results in the inappropriate proliferation of cells (Vander Heiden and DeBerardinis 2017; Pavlova and Thompson 2016). Metabolomics is the study of altered metabolites that are produced by cellular processes mediated by proteins and thus it is a direct assessment of phenotype. Plasma or serum samples from patients are the major focus for metabolomic analysis of cancer cells. The methodologies that are used for metabolomic studies for biomarker detection include mass spectrometry and Nuclear Magnetic Resonance (NMR)-based imaging techniques, such as Magnetic Resonance Spectroscopic Imaging (MRSI) which can use both tissue/cell or biopsies samples for detection (Schmidt et al. 2021). Some putative metabolite biomarkers are altered carbohydrates in acute myeloid leukemia and unsaturated free fatty acids in colon cancer (Chaturvedi et al. 2013; Zhang et al. 2016). Other metabolite biomarkers include changes in citric acid, branched-chain amino acid for prostate cancer and pancreatic cancer (Giskeødegård et al. 2013; Mayers et al. 2014). Bladder cancer biomarkers detected from urinary metabolic profiling and 27 differentially metabolites have been detected (Li et al. 2021b).

3.3.5 Epigenomics for Cancer Biomarker

Epigenomics can be defined as the study of genome-wide chemical modification such as acetylation and methylation of DNA. The epigenetic modifications play an important role in uncovering the important genetic marker as these modifications regulate cellular interactions (Piunti and Shilatifard 2016). ChIP seq and Whole-Genome Bisulfite Sequencing (WGBS) are the two important powerful techniques for the identification of DNA-binding sites of transcription factors and to detect the methylated part in the sequences respectively (Chakraborty et al. 2018; Raj et al. 2017). MBD-isolated Genome Sequencing (MiGS) is another novel technique that allows the analysis of whole-genome sequencing patterns (Serre et al. 2010). For predicting the risk of head and neck cancer, DNA methylation in saliva has been found to be a potential epigenetic biomarker (Rapado-González et al. 2021). Chromatin immunoprecipitation (ChIP) studies demonstrated RUNX1T1 as an epigenetic regulator of Small Cell Lung Cancer (SCLC) (He et al. 2021). A recent finding suggests that PD-L1 methylation in CpG loci can be considered as a valuable diagnostic biomarker for gastric cancer (Amini et al. 2021).

3.4 Bioinformatics Analytical Tools for Cancer Biomarker Discovery

The emerging high-throughput technologies result in the exponential growth of cancer biomarker data set from various resources. Thereby biologists face difficulty in extracting useful information from the available repositories as these contain various types of cancer-related information. The Cancer Genome Atlas (TCGA), a resource of multiomics cancer data platform that integrates genomics, epigenomics, and transcriptomics data of more than 30 human tumor types (Wang et al. 2016b). This aims to provide publicly available comprehensive atlas for molecular alteration in cancer cell. Pan-Cancer initiative is the new version of TCGA atlas and it is dedicated for comparison and analysis of molecular alteration found in different tumor types (Cancer Genome Atlas Research Network 2013). Recently published bioinformatics cancer-related database MarkerDB provides molecular cancer biomarker information along with the clinical significance such as diagnostic marker or prognostic marker (Wishart et al. 2021). Another database, OncoMX is a knowledge base; it integrates data for cancer mutation gene signatures, differential expression genes for cancer (Dingerdissen et al. 2020). Similarly, CIViCmine is another recently published database that provides list of curative clinically relevant cancer biomarkers information (Lever et al. 2019). Different machine learning and statistical approaches allow to the identification of biomolecules of interest from the large dataset with quantitative measurements. BioPlat is a software package for cancer biomarker discovery that allows high-throughput data filtering, gene expression calculation in silico (Butti et al. 2014). Another recently developed software Q omics that enables the analysis of patient survival, gene expression, and mutation of cancer-driven data set (Lee et al. 2021). There is an R-based tool available for cancer data analysis named CAncer bioMarker Prediction Pipeline (CAMPP), a standardized framework for the analysis of quantitative biological data (Terkelsen et al. 2020). Overall, several dedicated databases and tools are available for the storage and discovery of cancer biomarkers (Table 3.1).

Table 3.1 List of some important cancer-related databases and computational tools/software for cancer biomarkers storage and discovery
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3.5 Future Challenges

The future of biomarkers in oncology is potentially associated with the diagnostic, predictive, and prognostic cancer biomarkers. Despite the explosion of new technologies, a number of hurdles are associated with the identification of potential cancer biomarkers to be considered in clinical trials. The major challenges for cancer biomarker discovery can be considered at three levels (Henry and Hayes 2012; Teutsch et al. 2009). First is analytic validity which can be described as pre- and postanalytical evaluation of biomarker detection assay. It determines the specificity and sensitivity of the technical aspects (Hayes 2015). The second is clinical validity, it determines the diagnostic accuracy of biomarkers by dividing the population of interest into two groups such as patient and reference group (Bossuyt 2010). The third is clinical utility, which relates to making a clinical decision with a high level of evidence to improve cancer treatment outcomes (Hayes 2021). The aim of cancer research is earlier cancer diagnosis and get better clinical outcomes for precision oncology. Nevertheless, multiomics approaches offer great advantages for translational cancer research over monogenic markers. The rapid development of omics biomarkers increases the specificity of targeted therapeutic approach and leads to enhance predictive, preventive, and personalized medicine (PPPM) practice in clinical oncology (Lu and Zhan 2018).