Identification of key pathways and genes in the progression of cervical cancer using bioinformatics analysis.

Abstract

The aim of the present study was to investigate the key pathways and genes in the progression of cervical cancer. The gene expression profiles GSE7803 and GSE63514 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using GEO2R and the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery. The hub genes were identified using Cytoscape and protein-protein interaction (PPI) networks were constructed using the STRING database. A total of 127 and 99 DEGs were identified in the pre-invasive and invasive stages of cervical cancer, respectively. GO enrichment analysis indicated that the DEGs in pre-invasive cervical cancer were primarily associated with the ‘protein binding’, ‘single-stranded DNA-dependent ATPase activity’, ‘DNA replication origin binding’ and ‘microtubule binding’ terms, whereas the DEGs in invasive cervical cancer were associated with the ‘extracellular matrix (ECM) structural constituent’, ‘heparin binding’ and ‘integrin binding’. KEGG enrichment analysis revealed that the pre-invasive DEGs were significantly enriched in the ‘cell cycle’, ‘DNA replication’ and ‘p53 signaling pathway’ terms, while the invasive DEGs were enriched in the ‘amoebiasis’, ‘focal adhesion’, ‘ECM-receptor interaction’ and ‘platelet activation’ terms. The PPI network identified 4 key genes (PCNA, CDK2, VEGFA and PIK3CA), which were hub genes for pre-invasive and invasive cervical cancer. In conclusion, bioinformatics analysis identified 4 key genes in cervical cancer progression (PCNA, CDK2, VEGFA and PIK3CA), which may be potential biomarkers for differentiating normal cervical epithelial tissue from cervical cancer.