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Open Access Journal of Microbiology & Biotechnology Research Article 19 min read

Role of Bioinformatics in Identifying Potential Biomarkers of Cervical Cancer

Gupta P*, Sharma D and Hans C
* Corresponding author
ISSN: 2576-7771  10.23880/oajmb-16000252  Received: January 24, 2023  Published: February 23, 2023
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Keywords
Cervical Cancer Bioinformatics Tools miRNA Biomarkers
Abstract

Cervical cancer is the one of the leading causes of death among women worldwide. The cancer evolves over a longer period of time and can be screened by different laboratory tests. In developed countries although the rate of incidence and mortality has been reduced, in developing countries the incidence rate still remains high. A large number of factors have been reported that promote the risk of cervical cancer. Some host factors play crucial role in the induction of the malignant transformation. These include micro RNAs that have known to act as oncogenes or tumor suppressor genes in tumors of cervical cancer. Certain high throughput technologies have generated data on miRNAs that are significant in the pathogenesis of cervical cancer. The data has been investigated extensively with the help of tools of bioinformatics for identification of potential biomarkers of cervical cancer. In the current article, we aim to discuss the properties of the tools of bioinformatics that have paved the research on cervical cancer. It is concluded that there is a need of development of large number and various types of tools of bioinformatics facilitating identification of potential biomarkers of cervical cancer for early diagnosis and better treatment regimen.

Introduction

Cervical cancer is the fourth most common cancer affecting women worldwide after breast, colorectal and lung cancers [1]. In India, it is the most common cancer [2] that has precancerous stages and evolves over 10-15 years or more [3]. In a previous study, it has been reported that

43% of diagnosed patients fall below 45 years of age and 20- 28% fall below 40 years of age [4]. The screening of cervical cancer is done by cytology, visual inspection with acetic acid (VIA) and HPV DNA testing [5, 6, 7]. It is reported that the sensitivity of pap cytology ranges from 30% to 87% and the specificity ranges from 86% to 100% [8]. Amongst all the screening tests HPV DNA testing is the most reproducible screening test [9]. However, some of the disadvantages of the test such as cost, time-consumption and labor limit its use to more of research settings [10]. In developed countries, the rate of incidence and mortality has been reduced which is attributed to the rapid screening of the infected population. Whereas, in developing countries the incidence rate still remains high accounting 85% of all the cases. It has been seen that earlier the diagnosis, better is the patient prognosis and overall patient survival [11].

Several factors have been reported that promote the risk of cervical cancer. These include early age onset of sexual activity, multiple sexual partners, tobacco smoking, immunosuppressive medications and use of combined hormonal oral contraceptives for more than 5 years and infection with STDs such as HIV, herpes, Chlamydia, gonorrhea and syphilis [12, 13, 14, 15]. WHO recommends two prophylactic vaccines, a quadrivalent vaccine, Gardasil (Merck, USA) and a bivalent vaccine, Cervarix (Glaxo Smith Klein, Belgium) for prevention of HPV related diseases. However, development of a new vaccine is underway and it is a nonavalent vaccine (9-valent) which incorporates a total of nine HPV types [7].

Human Papilloma Virus (HPV) is the leading cause of cervical cancer [16]. Human Papilloma viruses belong to the family Papillomaviridae. The virions are non-enveloped with approximately 55 nm diameter in size and contain a double- stranded DNA genome of approximately 7.9 kb in length [17]. HPV transmission occurs through oral, vaginal, or anal sex with infected persons [18]. However, it can also be transmitted through mouth-to-mouth contact or by vertical transmission from infected mother to child during pregnancy [19]. The anal HPV infections are most prevalent in MSM (Men who have Sex with Men) and HIV infected individuals. In a study, transmission between hands and genitals, as well as apparent self-inoculation events (primarily in men) have been also reported Hernandez BY, et al. [20]. The prevalence rate of HPV is generally constant across age groups in India unlike most populations in developed countries [21, 22].

Although in majority of the cases of cervical cancer, infection with oncogenic subtypes of HPV-16 or -18 is reported, it is well defined that HPV infection alone is insufficient in malignant transformation. Other factors including host genes and subsequent genetic events independently or in concert with HPV infection are required in the induction of cervical cancer [3]. In comparison to risk in uninfected women, the risk of developing squamous cell carcinoma of the cervix is about 400 times higher following infection with HPV-16 and about 250 times higher in individuals infected with HPV-18 [7]. In men, the most common oncogenic genotypes include HPV-6, HPV-16 and HPV-52.

It is known that HPVs are epitheliotropic in nature and infect the cervical squamous epithelium through small abrasions in the tissue. The viral infection is then closely linked to keratinocyte differentiation. Initially, in the proliferating basal epithelial cells following viral infection, the viral genome is maintained as a low copy episome [23]. With progressive keratinocyte differentiation, viral genome amplification and gene expression increase until late “L” gene expression and virion production occur in the terminally differentiated superficial cells. This form of infection leads to koilocytosis, nuclear enlargement, dyskeratosis, and multinucleation and in some cases also low grade SIL [16].

It has been observed that viral oncogene deregulation, particularly integration of HR-HPV into the host genome plays a major role in HPV-related carcinogenesis as it is detected in 90% of all cervical carcinomas [24]. The HPV DNA integration results in elevated expression of HPV E6 and E7 oncoproteins [24]. These oncoproteins subsequently allows the virus to replicate through inhibitory effects on the tumor suppressor proteins p53 and pRb, respectively [25, 26]. The epidemiological studies from different locations of India show that HPV-16 is most prevalent and HPV-68 is least prevalent [27]. However, HPV-16 and 45 were more prevalent in Northern India compared to Southern India, whereas HPV-35 appeared to be more prevalent in Southern India compared to Northern India. The overall prevalence of HPV in India was found to be similar to high-risk areas in Latin America, but lower than that observed in some parts of sub-Saharan Africa [28]. It has been also reported that HPVs associated with CaCx have been found in 6% - 88% of females with CaCx in India [29, 30, 31, 32, 33]. It is clear that studies pertaining to population prevalence of HPV and cervical intraepithelial neoplasias (CIN) act as important indicators to evaluate disease burden in a community, monitor performance of CaCx screening program and assess impact of HPV vaccination program [34].

In a study, it has been reported that certain miRNAs act as oncogenes or tumor suppressor genes and exhibit altered expression profiles in cervical cancer tumors [35]. MicroRNAs (miRNAs / miRs) are small non-coding single stranded RNAs that regulate gene expression and play important role in regulating cellular proliferation, differentiation and apoptosis [36, 37]. A large amount of data has been generated on miRNAs utilizing high- throughput technologies. This publically available data has been investigated extensively with the help of tools of bioinformatics including Gene Expression Omnibus, GEO2R, DAVID (Database for Annotation, Visualization and Integrated Discovery), STRING (Search Tool for the Retrieval of Interacting Genes), miRecords, MERAV, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes), SAM (Significance Analysis of Microarrays), Cytoscape, Topp Gene database, OncoLnc etc. for identification of potential biomarkers of cervical cancer [38, 39, 40, 41].

In a study, potential biomarkers in cervical cancer were identified with combined public mRNA and miRNA expression microarray data analysis [11]. The study utilized database; Gene expression Omnibus. The tool GEO2R was used to screen genes as DEGs (Differentially Expressed Genes) and DEMs (Differentially Expressed microRNAs) between cervical cancer and normal tissue samples. The construction of Protein-protein interaction (PPI) network was carried- out by STRING database, functional pathway enrichment analysis of the DEGs and Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology studies were completed by DAVID. The miRecords tool was used to identify miRNAs and their target genes. Using OncoLnc tool, an overall survival analysis was conducted for patients having cervical cancer. The association of cervical cancer with the DEGs regulated by DEMs was studied by downloading all cervical cancer data from TCGA and analyzing the association between RhoB / STMN1 expression and survival time for patients with cervical cancer.

It was found that ‘Integrin-mediated’, ‘proteolysis’ and ‘phosphoinositide 3 kinase-protein kinase 3’ signaling pathways were most enriched in the DEGs. Three of the DEGs were validated as DEM target genes, including stathmin 1 (STMN1), Ras homolog family member B (RhoB) and cyclin D1 (CCNB1). Using OncoLnc survival analysis, it has been identified that STMN1 was associated with a significantly reduced overall survival, whereas RhoB was associated with a significantly longer overall survival time in patients having cervical cancer. Furthermore, the Cancer Genome Atlas (TCGA) revealed an association between the mRNA expression levels of STMN1 and RhoB, and the overall survival time for patients with cervical cancer. It was concluded that RhoB and STMN1 are key genes that may provide potential targets for cervical cancer diagnosis and treatment. In another study, similar analysis was carried- out [40]. Several other genes important in carcinogenesis of cervix have been identified using similar approach. For eg. DPP4, EDN3, FGF14, TAC1 and WNT16 have been suggested to be involved in the pathogenesis of cervical cancer [39].

Identifying host genes and the molecular mechanisms involved in pathogenesis of cervical cancer has become necessary to develop better treatment regimen. In the present study we aim to discuss the tools of bioinformatics that have been extensively utilized in identifying potential biomarkers of cervical cancer (Figure 1).

Figure 1: Different tools of bioinformatics for identification of potential biomarkers.
Click to enlarge
Figure 1: Different tools of bioinformatics for identification of potential biomarkers.

Bioinformatics Tools

Gene Expression Omnibus Database

It is a public depository that documents microarray, next- generation sequencing, and other forms of high-throughput functional genomics data. The database is an international archive. The three major aims of GEO are, providing a robust and versatile database to efficiently store high-throughput functional genomic data, offering simple formats and methods for data submission and finally providing user- friendly mechanisms enabling users to inquire, determine, analyse and download studies and gene expression profiles of interest (https://www.ncbi.nlm.nih.gov/geo/info/overview. html). The database has been used extensively for screening of Differentially Expressed Genes (DEGs) and micro RNAs (DEMs) involved in cervical cancer [38, 41].

GEO2R

It is one of the bioinformatics tools that permit users to analyse two or more than two groups of samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. The tool performs comparisons among original submitter-supplied processed data tables utilizing the GEOquery and limma R packages from the Bioconductor project. In addition, it provides a simple interface that allows users to perform R statistical analysis without expertise in command line. The results obtained through GEO2R are presented as a table of genes arranged in the order of significance [42].

David

DAVID is a Database for Annotation, Visualization and Integrated Discovery and is available online at http:// www.david.niaid.nih.gov. The database addresses the need of functional annotation of differentially expressed genes which is achieved through dissemination of biologically important information across large datasets and subsequent graphical display of the functional information. The four web-based analysis modules of DAVID are (i) Annotation tool – appends descriptive data from several public databases to list of genes (ii) GoCharts – assigns genes of interest based on user selected classifications and term specificity level to Gene Ontology functional categories (iii) KeggCharts – further assigns genes to KEGG metabolic processes and also enables visual display of biochemical pathway maps (iv) Domain Charts – groups genes according to PFAM conserved protein domains. Following analysis through these four web- based modules, the database enables the identification of the function of the genes [43].

String

Search Tool for the Retrieval of Interacting Genes (STRING) is a biological database of confirmed and predicted protein-protein interactions. In STRING, the associations between proteins are defined as, direct (physical) and indirect (functional). STRING aims to collect and integrate available experimental data on protein-protein interactions together with importing established pathways and protein complexes from selected databases. Additionally, the predictions stem from five other sources such as analysis of systemic co-expression, identification of selective signals across genomes that are common, automated text mining of the scientific literature, exchange of interaction knowledge amongst organisms based on gene orthology using in silico methods and previous knowledge in databases. The STRING resource is available online, at http://string-db.org/ and currently covers 96, 43,763 proteins derived from 2,031 organisms [44].

miRecords is an integrated resource for animal microRNA-target interactions and is available at http:// miRecords.umn.edu/miRecords. The current release of miRecords includes 1135 records of authenticated miRNA - target interactions between 301 miRNAs and 902 target genes in seven animal species. Using Predicted Targets component of miRecords stores, it predicted miRNA targets produced by 11 established miRNA target prediction programs. It is a useful resource for experimental miRNA researchers and informatics scientists developing the next- generation miRNA target prediction programs [45].

Merav

The tool stands for Metabolic Gene Rapid Visualizer and is available at http://merav.wi.mit.edu. MERAV can query a database composed of ~ 4300 microarrays. The microarray data represents human gene expression in normal tissues, cancer cell lines and primary tumors. It is a powerful tool for whole genome analysis which offers several advantages including genes search in parallel, comparison of gene expression among different tissue types as well as between normal and cancer cells, download of raw data, generation of heat maps and ultimately utilizing its internal statistical tools [46].

Gene Ontology (GO)

The Gene Ontology Consortium is a joint project of a total of three model organism databases: FlyBase16, Mouse Genome Informatics17 (MGI) and Saccharomyces Genome Database (SGD). The aim of the project is to produce a structured, precisely defined, common and controlled vocabulary for describing the roles of genes and gene products in any organism. The three categories of Consortium include biological processes, molecular function and cellular component. Since the development of Consortium, it has been used to describe all entities within an area of reality and all relationships between those entities. Ontology is composed of well-defined terms exhibiting well-defined relationships and thus may be a vital tool enabling researchers to turn data into knowledge [47].

KEGG

Kyoto Encyclopedia of Genes and Genomes (KEGG) tool is a systematic analysis of gene functions that links genomic information with higher order functional information. It includes three databases, GENES, PATHWAY and LIGAND. All the databases are daily updated and freely available at http://www.genome.ad.jp/kegg/. GENES database stores genomic information, PATHWAY database stores higher order functional information and LIGAND database stores information about chemical compounds, enzyme molecules and enzymatic reactions. It provides Java graphics tools for data analysis [48].

SAM

It is Significance Analysis of Microarrays (SAM) that detects statistically significant variation in expression of genes on a microarray. The tool functions by specifically assigning each gene a score based on its change in gene expression in relation to the standard deviation of repeated measurements for that gene. The operating system of the software is Linux/Unix, Mac OS and Windows with programming language, R [49].

Cytoscape

It is an open source software project that prepares a unified conceptual framework by integrating biomolecular interaction networks with high-throughput expression data and other molecular states. It`s software core provides basic functionality to query and layout the network; to visually integrate the network with expression profiles, phenotypes and other molecular states and to finally link the network to databases of functional annotations. The tool runs on all major operating systems and is freely available for download from Java application [50].

Topp Gene Database

The tool is available at http://toppgene.cchmc.org and free to all users. It is a one-stop portal for

  • Functional enrichment of gene list
  • Prioritization of candidate gene using either functional annotations or network analysis and
  • Identification and prioritization of novel disease candidate genes in the interactome. The computing includes a fuzzy-based similarity measure to determine the similarity between any two genes based on semantic annotations. The ToppGene Suite performs better than other three commonly used methods for candidate gene prioritization that are SUSPECTS [51], PROSPECTR [52] and ENDEAVOUR [53].

OncoLnc

The tool is known for maintaining survival data for 8,647 patients from 21 cancer studies performed by The Cancer Genome Atlas (TCGA), including RNA-seq expression for mRNAs and miRNAs from TCGA, and lncRNA expression from Mi Transcriptome beta. The record of survival data allow users to separate patients by gene expression and construct Kaplan-Meier plots for publication and also download the data for comprehensive analyses for further studies. Additionally, the tool keeps the record of precomputed survival analyses that allow users to probe survival correlations for up to 21 cancers in a single click [54].

Figure 2
Click to enlarge
Figure 2

Conclusion

It is concluded that there is a need of development of a large number and various types of tools of bioinformatics for identification of potential biomarkers of cervical cancer. It would facilitate diagnosis of cervical cancer at early stages and designing better treatment regimen. Finally, the survival rate of patients suffering from cervical cancer would increase and incidence rate would decrease.

Conflict of Interest

The authors declare that there is no potential conflict of interest.

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@article{gupta2023,
  title   = {Role of Bioinformatics in Identifying Potential Biomarkers of Cervical Cancer},
  author  = {Gupta P, Sharma D and Hans C},
  journal = {Open Access Journal of Microbiology & Biotechnology},
  year    = {2023},
  volume  = {8},
  number  = {1},
  doi     = {10.23880/oajmb-16000252}
}
Gupta P, Sharma D and Hans C (2023). Role of Bioinformatics in Identifying Potential Biomarkers of Cervical Cancer. Open Access Journal of Microbiology & Biotechnology, 8(1). https://doi.org/10.23880/oajmb-16000252
TY  - JOUR
TI  - Role of Bioinformatics in Identifying Potential Biomarkers of Cervical Cancer
AU  - Gupta P, Sharma D and Hans C
JO  - Open Access Journal of Microbiology & Biotechnology
PY  - 2023
VL  - 8
IS  - 1
DO  - 10.23880/oajmb-16000252
ER  -