Investigation of the hub genes and related mechanism in ovarian cancer via bioinformatics analysis

BACKGROUND: Ovarian cancer is a cancerous growth arising from the ovary. OBJECTIVE: This study was aimed to explore the molecular mechanism of the development and progression of the ovarian cancer. METHODS: We first identified the differentially expressed genes (DEGs) between the ovarian cancer samples and the healthy controls by analyzing the GSE14407 affymetrix microarray data, and then the functional enrichments of the DEGs were investigated. Furthermore, we constructed the protein-protein interaction network of the DEGs using the STRING online tools to find the genes which might play important roles in the progression of ovarian cancer. In addition, we performed the enrichment analysis to the PPI network. RESULTS: Our study screened 659 DEGs, including 77 up- and 582 down-regulated genes. These DEGs were enriched in pathways such as Cell cycle, p53 signaling pathway, Pathways in cancer and Drug metabolism. CCNE1, CCNB2 and CYP3A5 were the significant genes identified from these pathways. Protein-protein interaction (PPI) network was constructed and network Module A was found closely associated with ovarian cancer. Hub nodes such as VEGFA, CALM1, BIRC5 and POLD1 were found in the PPI network. Module A was related to biological processes such as mitotic cell cycle, cell cycle, nuclear division, and pathways namely Cell cycle, Oocyte meiosis and p53 signaling pathway. CONCLUSIONS: It indicated that ovarian cancer was closely associated to the dysregulation of p53 signaling pathway, drug metabolism, tyrosine metabolism and cell cycle. Besides, we also predicted genes such as CCNE1, CCNB2, CYP3A5 and VEGFA might be target genes for diagnosing the ovarian cancer.