Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Abstract

Background: Cervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer. Methods: The differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells. Results: A total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly upregulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells. Conclusion:In conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

“…On the other hand, we reviewed 52 published articles associated with CC infections that provided transcriptome-guided hub proteins (genomic biomarkers) for cross-validation of the proposed key genes and the candidate drug agents. There were 255 hub genes reported in those 52 articles, with 7 hub proteins (AURKA, PCNA, CCNB1, CDC45, MCM2, TOP2A, CDK1) appearing in at least 5 articles ( Table S2 ) [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 , 25 , 26 , 27 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Five (AURKA, CCNB1, MCM2, TOP2A, and CDK1) of the seven reported hub proteins were found to be similar to our suggested seven KGs.…”

“…CDK1 contributed to the occurrence and development of CSCC [ 18 , 23 , 25 , 26 , 27 , 40 , 41 , 44 , 46 ]. MCM2 involved in the carcinogenesis of cervical cancer [ 16 , 19 , 20 , 21 , 23 , 27 , 42 ]. TOP2A is regarded as a biomarker for the improved diagnosis of CC [ 16 , 17 , 19 , 23 , 24 , 26 , 27 , 41 , 43 , 45 ].…”

“…Hub genes/study genes mediated proteins have been considered as the key drug target receptors. Transcriptomics analysis is a widely used popular approach to explore genomic biomarkers [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. By this time, several authors have suggested several sets of hub genes/study genes to explore molecular mechanisms and pathogenetic processes of CC [ 22 , 23 , 24 , 25 ].…”

Research Progress of PCNA in Reproductive System Diseases

“…The volatile anesthetic, isoflurane, activates the histone deacetylase 6 pathway, accompanied by the upregulation of PCNA protein expression, thereby promoting cervical carcinogenesis [ 47 ]. PCNA was significantly correlated with shorter overall survival in patients with CC [ 48 ]. Lv et al [ 49 ] used CC as an example to conduct a systematic review and meta-analysis of the clinical effects of PCNA.…”

Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods

“…Dan et al identified PLOD2, ANLN, AURKA, and AR as possible therapeutic targets of cervical cancer through comprehensive analysis of cervical cancer-related gene expression profiles combined with bioinformatics [ 12 ]. Dan et al found that CDC45, GINS2, MCM2, and PCNA may be related to the occurrence and development of cervical cancer, and further verified that MCM2 knockdown can inhibit the proliferation of cervical cancer cells [ 13 ]. Although many studies have focused on certain genes in cervical cancer, it is still far from enough to reveal the underlying pathogenesis [ 14 – 16 ].Therefore, the present study used bioinformatics methods to find novel prognostic biomarkers of cervical cancer.…”