Investigation of differentially-expressed microRNAs and genes in cervical cancer using an integrated bioinformatics analysis.

Abstract

Cervical cancer is one of the most common types of cancer among women worldwide. In order to identify the microRNAs (miRNAs/miRs) and mRNAs associated with the carcinogenesis of cervical cancer, and to investigate the molecular mechanisms of cervical cancer, an miRNA microarray, GSE30656, and 3 mRNA microarrays, GSE63514, GSE39001 and GSE9750, for cervical cancer were retrieved from Gene Expression Omnibus. These datasets were analyzed in order to obtain differentially-expressed genes (DEGs) and miRNAs using the GEO2R tool. Gene Ontology (GO) and pathway enrichment analysis for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) analysis for DEGs was conducted using The Search Tool for the Retrieval of Interacting Genes software and visualized using Cytoscape, followed by hub gene identification, and biological process and pathway enrichment analysis of the module selected from the PPI network using the Molecular Complex Detection plugin. In addition, miRecords was applied to predict the targets of differentially-expressed miRNAs. A total of 44 DEGs and 15 differentially-expressed miRNAs were identified. These DEGs were mainly enriched in GO terms associated with the cell cycle. In the PPI network, cyclin-dependent kinase 1, topoisomerase DNA IIα, aurora kinase A (AURKA) and minichromosome maintenance complex component 2 (MCM2) had higher degrees of connectivity. A significant module was detected from the PPI network. AURKA, MCM2 and kinesin family member 20A exhibited higher degrees in this module, while the genes in the module were mainly involved in the cell cycle and the DNA replication pathway. In addition, estrogen receptor 1 was predicted as the potential target of 13 miRNAs. A total of 10 DEGs were identified as potential targets of miR-203. In conclusion, the results indicated that microarray dataset analysis may provide a useful method for the identification of key genes and patterns to successfully identify determinants of the carcinogenesis of cervical cancer. The functional studies of candidate genes and miRNAs from these databases may lead to an increased understanding of the development of cervical cancer.