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 up-regulated 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.
Keywords: cervical cancer: bioinformatics, DEGs, prognosis, hub genes, overall survival