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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<EMAIL_ADDRESS>
© 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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[54] Neugut AI, Jacobson JS, Rella VA. Prevalence and incidence of colorectal adenomas and cancer in asymptomatic persons. Gastrointestinal Endoscopy Clinics of North America. 1997;**7**(3): 387-399

[55] Diamandis EP. Towards identification of true cancer biomarkers. BMC Medicine. 2014;**12**(1):1-4

[56] Kulasingam V, Pavlou MP, Diamandis EP. Integrating highthroughput technologies in the quest for effective biomarkers for ovarian cancer. Nature Reviews Cancer. 2010;**10**(5): 371-378

#### **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**).

#### *<IP_ADDRESS> 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*

#### *<IP_ADDRESS> 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].

#### *<IP_ADDRESS> 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].

#### *<IP_ADDRESS> 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].