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ORIGINAL ARTICLE
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Discovery of novel six genes-based cervical cancer-associated biomarkers that are capable to break the heterogeneity barrier and applicable at the global level


1 Department of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
2 Department of Biochemistry and Biotechnology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

Date of Submission09-Sep-2021
Date of Acceptance22-Sep-2021
Date of Web Publication27-Apr-2022

Correspondence Address:
Yasir Hameed,
Department of Biochemistry and Biotechnology, The Islamia University of Bahawalpur, Bahawalpur
Pakistan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.jcrt_1588_21

 > Abstract 


Background: Although previous studies have identified the cervical cancer biomarkers by utilizing gene expression omnibus (GEO) expression datasets, they are limited in their application because of the heterogeneity-specific natures. Therefore, the present study was initiated to discover the GEO-based cervical cancer biomarkers that could employ over the heterogeneity barrier.
Methods: Initially, the already reported cervical cancer-associated hub genes were mined and extracted through the literature search. Then, a protein-protein interaction network was developed and analyzed to discover six more closely cervical cancer-linked hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to determine the diagnostic and prognostic potential of the real hub genes in cervical cancer patients of different clinicopathological features.
Results: From a pool of 110 collected hub genes, in total 6 genes (CDK1, CCNB1, topoisomerase 2 alpha, checkpoint kinase 1, AURKA, and CDC6) were regarded as the real hub genes. Expression analysis revealed the significant upregulation of the real hub genes in cervical cancer patients of different races, cancer stages, body weights, and age groups. Further, correlational analyses have shown different interesting correlations between the expression and various other parameters of the real hub genes including promoter methylation status, genetic alteration, overall survival duration, tumor purity, and CD8+ T immune cells infiltration. Finally, related to hub genes, we have also identified potential miRNAs and chemotherapeutic drugs showing a great therapeutic potential.
Conclusion: We have discovered a set of six real hub genes that might be utilized as new biomarkers for cervical cancer patients of different clinicopathological characteristics, overcoming the heterogeneity barrier.

Keywords: Biomarker, cervical cancer, heterogeneity, overall survival



How to cite this URL:
Khan M, Hameed Y. Discovery of novel six genes-based cervical cancer-associated biomarkers that are capable to break the heterogeneity barrier and applicable at the global level. J Can Res Ther [Epub ahead of print] [cited 2022 Dec 8]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=344235




 > Introduction Top


Globally, cervical cancer ranked 4th as the most commonly reported cancer subtype in women leading to approximately 527,600 new cases and 265,700 deaths annually.[1] In less developed countries, its incidence rate is even more frequent.[1] The major risk factors of cervical cancer development include the human papillomavirus (HPV) infection, high parity, cigarette smoking, long-term use of oral contraceptive pills, and having sex at a very young age.[2]

At present, the microarray technique has helped the researchers to analyze thousands of differentially expressed genes (DEGs), simultaneously,[3] playing a key role in the development of a specific disease. Taking the advantage of gene expression omnibus (GEO),[4] different groups of researchers have analyzed the cervical cancer microarray expression datasets through diverse methodologies to discover some potential biomarkers as hub genes. However, keeping in view, the fact that biomarkers are highly cancer stages, races, genders, ages, and subclasses-specific biomolecules, and knowing that GEO-based cervical cancer microarray expression datasets belong to the cervical cancer patients of diverse attributes including different races, cancer stages, body weights, and age groups, it is clinically impossible to use already identified GEO-based biomarkers in cervical cancer patients of all the races, cancer stages, body weights, and age groups over the heterogeneity barrier.

Therefore, based on already reported biomarkers (hub genes) from GEO microarray expression datasets of cervical cancer, it is required to discover a system of some potential diagnostic and prognostic biomarkers that could commonly be employed in cervical cancer patients of all the races, cancer stages, body weights, and age groups over the heterogeneity barrier for the better management of the disease.

For the said purpose, we will reanalyze the previously reported cervical cancer-associated hub genes through novel bioinformatics approach to discover a system of few potential genes (real hub genes) that could be utilized as diagnostic and prognostic biomarkers in cervical cancer patients regardless of heterogeneity barrier. To do so, we will initially extract the already reported cervical cancer-associated hub genes from the available studies which used GEO datasets of cervical cancer. Then, the extracted hub genes will be pooled to obtain a set of most significant dysregulated hub genes (in terms of the degree of centrality) from a large number of GEO-based cervical cancer datasets. And later, the obtained pool of hub genes will be subjected to pathway enrichment, protein-protein interaction (PPI) network construction, hub genes screening to identify the most centralized real hub genes (hub genes of hub genes) and their underlying pathways.

Next, the variations in the expression of real hub genes will be documented and validated through multiple online platforms including the GEPIA database[5] (available at; http://gepia.cancer-pku.cn/), DriverDBv3 (available at; http://driverdb.tms.cmu.edu.tw/), UALCAN database[6] (available at; http://ualcan.path.uab.edu/), and Human Protein Atlas (HPA, https:/www. proteinatlas. org/).[7] Furthermore, we also aim to perform the correlation analysis of the identified real hub genes expressions with promoter methylation level, genetic alterations, copy number variations (CNVs), overall survival (OS), tumor purity, and CD8+ T immune cells infiltration in cervical cancer. In addition, the miRNA-real hub genes co-regulatory network and real hub gene-drug interaction network will also be constructed. The present study might be helpful to establish a complete system of biomarkers that can be employed commonly to the cervical cancer patients of different races, cancer stages, body weights, and age groups over the heterogeneity-specific barrier.


 > Methods Top


Literature mining and hub genes extraction

A systematic search was performed using the PubMed search engine to identify the relevant studies which dealt with the GEO-based cervical cancer microarray expression datasets to identify the hub genes until June 2021. The search was done using the two keywords “Hub genes AND cervical cancer” and “Hub genes AND cervical neoplasia” with the “Original article” filter. A total of 88 articles were appeared, which were further filtered out for only 15 studies that collectively used 16 GEO-based cervical cancer microarray expression datasets and identified several hub genes. Following that, we extracted and combined all the hub genes reported in these studies to get a consolidated pool.

Pathway enrichment analysis

The GO and KEGG analysis are used to systematically analyze the gene functions and linking the genomic information to higher-order of the DEGs.[8] In this study, we utilized the DAVID 9th tool (https://david.ncifcrf.gov/) to perform the KEGG analysis of the hub genes[9] with default settings. In this analysis, a P < 0.05 was also adjusted as statistically significant.

Protein–protein interaction network construction and screening of real hub genes

The Search Tool for the Retrieval of Interacting Genes (STRING, http://www.stringdb.org/) is an online resource to calculate the information of PPIs networks.[10] In our study, we used this resource to construct a PPI network of the hub genes with a confidence score of ≥0.7. Later, this constructed PPI network was explored through Cytoscape software (v. 3.51) (19), in which Cytohubba application has helped us to identify the top six real hub genes through the degree method. We also performed KEGG pathway enrichment analysis of the real hub genes through DAVID tool with default settings. A P < 0.05 was set as the filter criterion.

Real hub genes survival and expression analysis via GEPIA

GEPIA (http://gepia.cancer-pku.cn/) platform offers easy access to the many key customizable and interactive features, such as differential expression analysis, survival analysis, and similar gene detection in patients of various cancer subtypes.[5] In our study, using GEPIA, we utilized the TCGA CESC dataset consisting of 306 cancerous and 13 normal samples to document variations in the expression levels as well as prognostic values of the real hub genes. For statistics, GEPIA used a Student's t-test and normalized the obtained expression as transcript per million (TPM) reads. P < 0.05 was chosen statistically significant.

Real hub genes expression validation via DriverDBv3

DriverDBv3 (http://driverdb.tms.cmu.edu.tw/) is a cancer omics-based database which includes the RNA expression, miRNA expression, methylation, CNV, and somatic mutation-related information.[11] We used DriverDBv3 database in this study to further validate the differential expression of the real hub genes using the new independent cohort of CESC patients. The expression level in DriverDBv3 was normalized as TPM reads, and a P < 0.05 was chosen as statistically significant.

Real hub genes expression validation via UALCAN

UALCAN (http://ualcan.path.uab.edu/) is the publically available tool specialized in the TCGA or published cancer-related multi-omics data mining and analysis.[12] In this study, we utilized UALCAN to validate the expression of real hub genes in CESC patients of different races, cancer stages, body weights, and age groups. For statistics, these tools also used a Student's t-test and normalized the obtained mRNA expression as TPM reads. A P < 0.05 was chosen as statistically significant.

Differential expression analysis of the real hub genes through human protein atlas database analysis

HPA (https:/www.proteinatlas.org/) is a database, that aims to quantify and locate all the human proteins in cells, tissues, and organs using the immunohistochemistry technique.[7] In the current study, HPA was utilized to examine the proteomics expression level of the real hub genes in CESC and normal tissue. The observed protein expression level was graded as not detected, low, medium, and high, based on the intensity of staining and fraction of the stained cells.

Correlation analysis between real hub genes expressions and their corresponding promoter methylation levels in cervical squamous cell carcinoma

MEXPRESS (https://mexpress.be/) database is principally developed to visualize the TCGA expression data and to identify the correlation between gene expressions and their corresponding promoter methylation levels in a variety of cancers.[13] In our study, the correlations between real hub genes expressions and their corresponding promoter methylation levels in CESC were computed through MEXPRESS using Pearson correlation analysis. P < 0.05 was chosen as statistically significant.

cBioPortal analyses

The cBioPortal (http://www.cbioportal.org) is specialized platform to retrieve and analyze cancer multi-omics data from TCGA projects.[14] This platform provides an easy access to a variety of online analyses including mRNA expression and genomic alteration analysis. In this study, we used cBioPortal to analyze the real hub genes-associated genomic alteration and mutational hotspots in cervical cancer patients.

Tumor purity, CD8+ T-immune cells infiltration, and real hub genes expression in cervical squamous cell carcinoma

TIMER database (https://cistrome.shinyapps.io/timer/) utilizes the RNA-Seq based expression data to find immune cells infiltration in cancer samples.[15] In our study, the associations between tumor purity, CD8+ T immune cells infiltration, and real hub genes expression were evaluated through TIMER database. P < 0.05 was chosen statistically significant.

miRNA-real hub gene co-regulatory network analysis

For the construction of miRNA-real hub genes interaction network, we used Encyclopedia of RNA interactomes (ENCORI, http://starbase.sysu.edu.cn/panCancer.php) database,[16] which collectively gathered information from four different databases including PITA, Tragetscan, miRanda, and PicTar for identifying the mRNA targeted miRNAs. In addition, we also employed the Cytoscape (3.8.2) to construct, visualize, and analyze the interaction network between targeted mRNAs and real hub genes.

Real hub gene-drug interaction network analysis

The Comparative Toxicogenomics Database (CTD, http://ctdbase.org/) was used to retrieve the information of chemotherapeutic drugs that could reduce or enhance the mRNA or protein expression levels of the genes of interest.[17] Briefly, all the real hub genes were searched in the CTD database, and real hub gene-drug interaction networks were visualized using Cytoscape software to identify the possible drugs that could decrease or increase the expression of the real hub genes in the treatment of CESC.


 > Results Top


Literature search and hub genes extraction

A total of 15 original studies were shortlisted, identifying hub genes in individual CESC microarray expression dataset[18],[19] or a combination of different CESC microarray expression datasets.[20],[21] Following this, the extraction and pooling of hub genes reported in these studies was done manually to obtained a consolidated pool of 110 dysregulated hub genes from 16 microarray datasets containing 323 cervical cancer samples and 141 normal controls [Table 1]. Original data without normalization are shown in [Supplementary Table 1].
Table 1: Summery of the cervical cancer microarray expression datasets and the hub genes extracted from the previous studies

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KEGG pathway enrichment of the hub genes

KEGG pathway enrichment analysis revealed that pooled hub genes are mainly involved in various diverse pathways including “Cell cycle,” “Pathways in cancer,” “p53 signaling pathway,” “Oocyte meiosis,” and “Hepatitis B” etc. The top 10 pathways associated with the hub genes are enlisted in [Figure 1] and [Table 2].
Figure 1: KEGG enrichment analysis across 110 identified hub genes related to CESC. CESC = Cervical Squamous Cell Carcinoma

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Table 2: Details of the KEGG pathway analysis of all the 110 pooled hub genes extracted from the various gene expression omnibus microarray expression datasets of cervical squamous cell carcinoma

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Protein-protein interaction network construction, screening of real hub genes, and their KEGG pathway enrichment

The obtained pool of 110 hub genes was subjected to PPI network construction through online available STRING database. This tool has helped us to obtain a PPI network containing 102 nodes and 1267 edges. Then, a Cytohubba analysis from Cytoscape software has further helped us to identify the more cervical cancer relevant few genes (real hub genes). Via degree method, the identified six real hub genes includes CDK1, CCNB1, topoisomerase 2 alpha (TOP2A), checkpoint kinase 1 (CHEK1), AURKA, and CDC6 [Figure 2] and [Table 3]. Furthermore, the KEGG pathway analysis of the identified real hub genes demonstrated the enrichment of four genes in three diverse pathways including “Cell cycle,” “p53 signaling pathway,” and “Oocyt meiosis” pathways [Table 4].
Figure 2: PPI network construction, screening of hub genes and their pathway enrichment. (a) A PPI network of hub genes, (b) Six identified real hub genes based on the degree of centrality, and (c) Pathway enrichment of the hub genes. PPI = Protein-protein interaction, TOP2A = Topoisomerase 2 alpha, CHEK1 = Checkpoint kinase 1

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Table 3: List of the real hub genes identified from the protein-protein interaction network of the extracted 110 cervical squamous cell carcinoma related hub genes

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Table 4: Details of the KEGG pathway analysis of the identified real hub genes related to cervical cancer

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Various specialized databases-based expression analysis and validation of the real hub genes in normal controls and cervical squamous cell carcinoma patients

To analyze and validate the mRNA and protein expression of the real hub genes in normal controls and CESC patients of different clinicopathological features including different races, cancer stages, body weights, and age groups, we utilized four different reliable platforms including GEPIA; it retrieved mRNA data from UCSC Xena server which contained 306 CESC samples paired with 13 normal controls and was used in our study for the mRNA expression analysis of the real hub genes, DriverDBv3 database; it retrieved data from TCGA database which contained 411 CESC and 57 normal samples and was used in our study for the mRNA expression validation of the real hub genes, UALCAN database; it also retrieved mRNA data from TCGA database which contained 303 CESC samples paired with 03 normal samples and was utilized in this study to validate the real hub genes expression in different clinicopathological features of CESC patients, and finally HPA database; it contained immunohistochemically verified proteomics data and was used in the present study to validate real hub genes overexpression at the proteomics level. Taken together the results of mRNA and protein expression analysis and validation using four different platforms (GEPIA, DriverDBv3, UALCAN, and HPA), we confirmed the significant (P < 0.05) up-regulation of the all six real hub genes at both mRNA and protein level in CESC patients of different races, cancer stages, body weights, and age groups relative to controls [Figure 3], [Figure 4], [Figure 5].
Figure 3: Box plots showing the mRNA expression levels of real hub genes in normal and CESC samples via GEPIA, DriverDBv3, and UALCAN databases. (a) GEPIA based mRNA expression levels of real hub genes, (b) DriverDBv3 based mRNA expression levels of real hub genes, and (c) UALCAN based mRNA expression levels of real hub genes. *P < 0.05. CESC = Cervical Squamous Cell Carcinoma

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Figure 4: Box plots showing real hub genes mRNA expression levels in normal and CESC patients stratified by different races, stages, body weights, and age groups via UALCAN database. (a) Different races based expression, (b) different cancer stages based expression, (c) different body weights based expression, and (d) different age groups based expression. *P < 0.05. CESC = Cervical Squamous Cell Carcinoma

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Figure 5: Translation expression of the real hub genes across CESC tissues and normal controls taken from HPA database (×200). (a) CDK1, (b) CCNB1, (c) TOP2A, (d) CHEK1, (e) AURKA, and (f) CDC6. The level of staining is directly proportional to the level of protein. HPA = Human Protein Atlas, TOP2A = Topoisomerase 2 alpha, CHEK1 = Checkpoint kinase 1, CESC = Cervical Squamous Cell Carcinoma

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Survival analysis of the real hub genes in the cervical squamous cell carcinoma patients

Survival analysis of the real hub genes in CESC patients was done through GEPIA database. In the analyzed datasets, a total of 292 samples of CESC were included for the analysis. In view to the results of this analysis, it was observed that the higher expression of the real hub genes including CDK1 (hazard ratio [HR]: 0.83, P > 0.05), CCNB1, (HR: 0.79, P > 0.05), TOP2A (HR: 0.59, P < 0.05), CHEK1 (HR: 1, P > 0.05), AURKA (HR: 1.3, P > 0.05), and CDC6 (HR: 0.96, P > 0.05) were not linked with the OS duration of the CESC patients, therefore, they are supposed to be the worst prognostic biomarkers for analyzing OS of the CESC patients [Figure 6].
Figure 6: The prognostic information of the real hub genes in CESC patients obtained via GEPIA database. (a) The calculated prognostic value of CDK1 in CESC patients (b) the calculated prognostic value of CCNB1 in CESC patients, (c) the calculated prognostic value of TOP2A in CESC patients (d) the calculated prognostic value of CHEK1 in CESC patients, (e) the calculated prognostic value of AURKA in CESC patients, and (f) the calculated prognostic value of CDC6 in CESC patients. Blue color indicates this low expression while red color indicates the high expression of a gene. Blue color indicates this low expression while red color indicates the high expression of a gene. *P < 0.05. TOP2A = Topoisomerase 2 alpha, CHEK1 = Checkpoint kinase 1, CESC = Cervical Squamous Cell Carcinoma

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Pearson correlation analysis between promoter methylation level and mRNA expression of the real hub genes in cervical squamous cell carcinoma patients

Promoter methylation participates in the expression regulations of the DNA repair and tumor suppressor genes and also closely associated with the initiation of cancer.[33] Therefore, in our study, we investigated the association of promoter methylation levels with real hub genes expression in CESC patients relative to normal controls through MEXPRESS platform. Our results have revealed a significant (P < 0.05) negative correlation between real hub genes expression and their promoter methylation levels in CESC patients [Figure 7].
Figure 7: MRXPRESS based correlation analysis between real hub genes expression and their promoter methylation levels in CESC. (a) A correlation analysis between CDK1 expression and its promoter methylation levels in CESC, (b) A correlation analysis between CCNB1 expression and its promoter methylation levels in CESC, (c) A correlation analysis between TOP2A expression and its promoter methylation levels in CESC, (d) A correlation analysis between CHEK1 expression and its promoter methylation levels in CESC, (e) A correlation analysis between AURKA expression and its promoter methylation levels in CESC, and (f) A correlation analysis between CDC6 expression and its promoter methylation levels in CESC. A negative sign indicates the negative correlation between SHMT2 expression and its promoter methylation using a specific probe at a specific CpG island. *P < 0.05. CESC = Cervical Squamous Cell Carcinoma

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Real hub genes-associated genetic variations in cervical squamous cell carcinoma

Information related to genetic variations in identified real hub genes was obtained via cBioPortal platform from a TCGA CESC datasets (Cervical Squamous Cell Carcinoma TCGA, PanCancer Atlas = 278 samples) [Figure 8]. We have observed a varying degrees of genetic variations in the real hub genes, out of which, CHEK1 has shown the highest incidence rate 4% (11/278) of genetic variations and harbors maximum deep deletion abnormalities, followed by this, AURKA has shown the second highest genetic variations rate 2.9% (8/278) and harbors maximum missense mutations. While other real hub genes, including TOP2A, CDC6, CDK1, and CCNB1 have shown a genetic variations rates of 2.2% (6/278), 1.1% (3/278), 0.7% (2/278), and 0% (0/1482) in CESC samples, respectively. In TOP2A, the most frequently observed genetic alterations were missense mutations, while in CDC6 and CDK1, the most frequent genetic alterations were deep deletion [Figure 8].
Figure 8: Frequency of the genetic alterations and CNVs related to the real hub genes in CESC patients. CNVs = Copy number variations, CESC = Cervical Squamous Cell Carcinoma

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Tumor purity, CD8+ T-immune cells infiltration, and real hub genes expression in cervical squamous cell carcinoma patients

Tumor purity and CD8+ T immune cells infiltration are the important factors for designing an appropriate immunotherapy.[34] In our study, a Spearman correlation between tumor purity, CD8+ T immune cells infiltration, and real hub genes expression in CESC was evaluated through TIMER. In view to the results of this analysis, a significant (P > 0.05) positive correlation was revealed between the mRNA expression of the real hub genes and tumor purity in CESC [Figure 9]. Moreover, our results have also shown a significant (P > 0.05) positive correlation between the mRNA expression of the real hub genes and CD8+ T-immune cells infiltration in CESC. Taken together, these results have shown that there is a significant association between tumor purity, CD8+ T immune cells infiltration, and real hub genes expression in the pathogenesis of CESC.
Figure 9: TIMER based Spearman correlational analysis between tumor purity, CD8+ T immune cells infiltration, and real hub genes expression in CESC. (a) TIMER based Spearman correlational analysis between CD8+ T immune cells infiltration and expression of CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 in CESC, and (b) TIMER based Spearman correlational analysis between CD8+ T immune cells infiltration and expression of CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 in CESC. Red color box represents the positive correlation, blue color represents the negative correlation while white color represents no correlation. *P < 0.05. TOP2A = Topoisomerase 2 alpha, CHEK1 = Checkpoint kinase 1, CESC = Cervical Squamous Cell Carcinoma

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miRNAs-real hub genes co-regulatory network analysis

In this study, ENCORI and Cytoscape were used to constructed the miRNAs-real hub genes co-regulatory network. In the network, total numbers of miRNAs and mRNAs were 66 and 5, respectively. In addition, via degree method we have identified 4 miRNAs including miR-205-5p, miR-124-3p, miR147a, and miR-34a-5p that all the five real hub genes. It is already acknowledge by the previous studies that miR-205-5p/CYR61, miR-124-3p/IGF2BP1, miR147a/IGF1R, and miR-34a-5p/BCL2 axis are the potential biomarkers are being used for predicting the diagnosis and prognosis of the cervical cancer.[35],[36],[37],[38] However, our results suggested that miR-205-5p/CDK1, CCNB1, TOP2A, CHEK1, AURKA and CDC6, miR-124-3p/CDK1, CCNB1, TOP2A, CHEK1, AURKA and CDC6, miR147a/CDK1, CCNB1, TOP2A, CHEK1, AURKA and CDC6, and miR-34a-5p/CDK1, CCNB1, TOP2A, CHEK1, AURKA and CDC6 axis can also be used as a novel potential diagnostic and prognostic biomarkers to predict the diagnosis and prognosis of the cervical cancer. Moreover, the identified axis can also be used as therapeutic targets in the treatment of cervical cancer for regulating the gene expression of the real hub genes [Figure 10].
Figure 10: The miRNA–real hub genes co-regulatory network in cervical cancer. (a) An overall miRNA-real hub genes co-regulatory network identified via RegNetwork database, and (b) A co-regulatory network between real hub genes and their top four targeted miRNAs. Blue nodes represented the miRNAs, grey color nodes represents the top four identified miRNAs, while the orange nodes the hub gene. The lines represented the interaction between the miRNAs and real hub genes

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Gene-drug interaction network analysis of the real hub genes

In order to explore the relationship between real hub genes and available cancer therapeutic drugs, a gene-drug interaction network was developed using CTD database. The expression of identified real hub genes including CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 could potentially influence by a variety of drugs, for example, Alvocidib and Calcitriol could reduce the expression level of CDK1 while Camptothecin and Acrylamide could elevate CCNB1 expression level [Figure 11].
Figure 11: Gene-drug interaction network of the real hub genes. Panels A–F indicates available chemotherapeutic drugs that decrease or increase the expression levels of the real hub genes. (a) CDK1, (b) CCNB1, (c) TOP2A, (d) CHEK1, (e) AURKA, and (f) CDC6. Red arrows indicate the chemotherapeutic drugs that could increase the expression level of the real hub genes. While green arrows indicate the chemotherapeutic drugs that could decrease the expression level of the real hub genes. The numbers of arrows between chemotherapeutic drugs and real hub genes in the network represent the supported numbers of literatures by previous studies. TOP2A = Topoisomerase 2 alpha, CHEK1 = Checkpoint kinase 1

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 > Discussion Top


Cervical cancer is the 4th most prevalent female malignancy that contributes to the high mortality rate each year.[39] Although previous studies have identified the cervical cancer biomarkers by utilizing GEO expression datasets for the better management of the disease, they are limited in their application because of the heterogeneity-specific natures. Therefore, the present study was initiated to discover the GEO-based cervical cancer biomarkers that could employ over the heterogeneity barrier.

For this purpose, we have carried out a PubMed-based search strategy to identify the studies which utilized the cervical cancer microarray datasets from the GEO database to explore potential diagnostic and prognostic biomarkers (hub genes). A total of 21 studies were selected which collectivity utilized more than 15 cervical cancer microarray datasets. We manually extracted and combined all the identified hub genes from these studies to get a consolidated pool of 110 hub genes.

Then, we processed these pooled 110 hub genes for pathway enrichment analysis and PPI network construction to identify six more closely cervical cancer-related hub genes (real hub genes). We further performed the expression analysis and validation of the real hub genes, and we also aim to perform the correlation analysis of the identified real hub genes expressions with promoter methylation level, genetic alterations, OS, tumor purity, and CD8+ T immune cells infiltration in CESC. In addition, the miRNA-real hub genes co-regulatory network and real hub gene-drug interaction network will also be constructed.

KEGG pathway enrichment analysis revealed that most of the pooled hub genes were significantly (P < 0.05) enriched in various pathways including “Cell cycle,” “Pathways in cancer,” “p53signaling pathway,” “Oocyte meiosis,” and “Hepatitis B” pathways [Figure 1] and [Table 2]. A PPI network of all the pooled 110 hub genes illustrated the overview of their functional connections, of which top six real hub genes were selected as CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 through degree method [Figure 2] and [Table 3]. KEGG pathway analysis of the six real hub genes demonstrated that they are significantly (P < 0.05) enriched in different pathways including “Cell cycle,” “p53 signaling pathway,” and “Oocyt meiosis” pathways.

The first identified real hub gene cyclin-dependent kinase 1 (CDK1) is a well-conserved protein and acts as a serine/threonine kinase. It is a key player involved in controlling the cell cycle progression by targeting the approximately 70 different regulatory targets.[40] It is already acknowledged that HPV infection is one of the significant factors involved in the carcinogenesis of cervical cancer. After HPV infection, the E6 and E7 viral proteins inhibit the p53 dependent cell apoptosis and results in disrupted cell cycle regulations by inducing prompt p53 proteasomal degradation.[41] The p53 protein regulates the transcription of various downstream genes involved in apoptosis and cell cycle arrest. Under normal conditions, the p53 protein negatively regulates the transcription of CDK1.[42] It is earlier reported across various studies based on different populations that abnormally functional p53 due to HPV infection in cervical cancer results in the overexpression of CDK1;[43],[44],[45] however, no study has yet reported its clinicopathological feature-specific expression profiling. In our study, we documented its significant (P < 0.05) upregulation in CESC patients of different clinicopathological features as compared to the normal controls.

CCNB1 is a core member of the cyclin family and participates in the initiation of a robust quality control step of mitosis.[46] Recent studies based on different populations have suggested the involvement of CCNB1 in checkpoint control, who's dysfunctioning is considered as an early event in the pathogenesis of cancer. CCNB2 elevated expression has already been observed in cervical cancer and various other human cancers including lung cancer, esophageal squamous cell carcinoma, breast cancer, and melanoma;[47],[48],[49] however, no study has yet reported the clinicopathological feature-specific expression of CCNB1 in cervical cancer. In the present study, we observed its significant (P < 0.05) overexpression in CESC subjects of different clinicopathological features as compared to the normal controls.

DNA TOP2A is a nuclear enzyme that changed the DNA topology by interacting with its double-helix structure, thus played a key role in DNA replication, transcription, condensation, recombination, and segregation.[50] Different populations-based studies already reported TOP2A overexpression in cervical cancer and considered it as a biomarker of cervical carcinogenesis; however, no study has yet reported its clinicopathological feature-specific expression.[51],[52] In the current study, we found its significant (P < 0.05) up-regulation in CESC patients of different clinicopathological features relative to normal controls.

CHEK1 is one of the major signal transduction pathways regulators. These pathways mainly set in response to DNA damage response. Significant efforts have been made to comprehend the CHEK1 regulation and its role in cancer biology, and treatment.[53],[54],[55] In previous studies, it has been already acknowledged that CHEK1 up-regulate in cervical, breast, gastric, and lung cancers,[56],[57],[58],[59],[60] however, no study has yet reported its clinicopathological feature-specific expression in cervical cancer. In the present study, we found its significant (P < 0.05) up-regulation in CESC patients of different clinicopathological features as compared to the normal controls.

Aurora proteins are part of a small serine/threonine kinase family that are the major regulators of different mitosis and meiosis steps.[61] Since the discovery of AURKA, various studies have been carried out so far to document the role of its dysregulation in cancer development. The first data obtained regarding the role of AURKA dysregulation in the pathogenesis of cancer suggested that it was overexpressed in various cancers including colon cancer, primary breast, and colon tumor samples.[62] After that, many subsequent studies have been carried out to evaluate the AURKA expression level in cervical cancer but still, its role is poorly understood. In our study, we found that AURKA was significantly (P < 0.05) overexpressed in CESC patients of different clinicopathological features relative to normal controls.

CDC6 is an ATP binding protein and has played a crucial role in DNA replication initiation.[63] Several previous studies have reported the overexpression of CDC6 in cervical and different other subtypes of cancer.[64],[65] However, so far, no study has yet evaluated the clinicopathological feature-specific expression of CDC6 in cervical cancer. However, in the present study, we found the significant (P < 0.05) overexpression of CDC6 in CESC patients of different clinicopathological features as compared to the normal controls. Taken together the expression profiling of the real hub genes, we have suggested that upregulation of these genes (CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6) may serve as a potential biomarkers for CESC patients of different races, cancer stages, body weights, and age groups.

The results of genetic alterations and CNVs analysis revealed that real hub genes genetically altered or gain and loss their copies in the very least proportion of the CESC patients which probably indicated the no role of genetic alterations and CNVs in the expression variations of the real hub genes. Furthermore, the correlational analysis between the mRNA expression and promoter methylation level of the real hub genes in CESC revealed the expected significant (P < 0.05) negative correlation. Therefore, it is speculated that promoter hypomethylation might played a significant role in the up-regulation of real hub genes in CESC.

The results of correlational analysis between the mRNA expression of the real hub genes and OS duration of the CESC showed that overexpression of the real hub genes served as a worst prognostic factor for measuring the OS of the CESC patients. However, the OS data of all the real hub genes except CCNB1 were insignificant; therefore, further in-depth research is recommended to reveal the significant association between the mRNA expression and OS of the CESC patients.

To further clarify the underlying mechanism of real hub genes in CESC tumorigenesis, we performed the correlation analysis between the tumor purity, CD8+ T immune cell infiltration, and real hub genes expression in CESC. Results have shown that real hub genes have a positive correlation with the tumor purity in CESC which further confirmed that higher proportion of tumor cells in CESC is linked with the real hub genes overexpression. The CD8+ T immune cells are known as the major drivers of the anticancer immunity,[66] and earlier, CD8+ T cell immune infiltration was utilized as a diagnostic marker for the early detection of laryngeal squamous cell carcinoma (LSCC).[67] Furthermore, Trojan et al. have also successfully used CD8+ T cells immune infiltration for the personalized immunotherapy trials in LSCC.[68] Our results revealed the significant (P > 0.05) negative correlations between the mRNA expression of all the real hub genes (CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6) and CD8+ T immune cells infiltration in CESC. Taken together, these correlations have highlighted the new aspect of the real hub genes in ECSC tumorigenesis through linking with the CD8+ T immune cells infiltration. Best to our knowledge, this study is the first study to investigate the spearman correlation between the expression of real hub genes (CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6) and CD8+ T cells immune infiltration in CESC. These correlations may bring new ideas for the treatment of CESC patients who do not benefit from the existing immune checkpoint inhibitors/regulators.

Through miRNA-real hub genes co-regulatory analysis, we observed that four miRNAs including miR-205-5p, miR-124-3p, miR147a, and miR-34a-5p target all the six real hub genes. It is already reported that miR-205-5p/CYR61, miR-124-3p/IGF2BP1, miR147a/IGF1R, and miR-34a-5p/BCL2 axis are the potential biomarkers for predicting the diagnosis and prognosis of the CESC.[35],[36],[37],[38] In view to the results of the present study, we suggested that miR-205-5p/CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6, miR-124-3p/CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6, miR147a/CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6, and miR-34a-5p/CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 axis can also be used as a novel potential diagnostic and prognostic biomarkers to predict the diagnosis and prognosis of the CESC. Moreover, the identified axis can also be used as therapeutic targets in the treatment of CESC for regulating the gene expression of the real hub genes. Best to our knowledge, this study is the first study to report the tumorigenesis role of miR-205-5p, miR-124-3p, miR147a, miR-34a-5p together with CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 in CESC. In addition, we have identified few drugs could target the real hub genes and may affect the abnormal expression level and could be a potential part of therapeutic strategies.


 > Conclusion Top


In summary, we have identified a system of the six differentially expressed real hub genes including CDK1, CCNB1, TOP2A, CHEK1, AURKA, and CDC6 and their underlying molecular pathways in the CESC patients that could likely be employed as a possible molecular biomarkers of the CESC patients belonging to different races, cancer stages, body weights, and age groups over the heterogeneity-specific barrier. However, there is still need to verify the accuracy and validity of these biomarkers through voluminous testing prior to clinical implication.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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