Identification of Potentially Therapeutic Target Genes in Ovarian Cancer via Bioinformatic Approach

Abstract

Objective: To identify potentially therapeutic target genes involved in the pathogenesis of ovarian cancer using bioinformatic approach. Methods: The GEO2R online tool was employed to analyze the gene expression profiles of ovarian cancer. GO and KEGG enrichment analysis was utilized to annotate differentially expressed genes (DEGs). STRING database was employed to construct a protein-protein interaction (PPI) network with the DEGs. The PPI network interaction information was then visualized using Cytoscape software and ovarian cancer hub genes were identified based on Maximal Clique Centrality (MCC) algorithm. The identified hub genes were then analyzed with Kaplan Meier plotter to check their role on survival time of ovarian cancer patients. Results: Differentially expressed analysis resulted in 332 DEGs, of which 340 were down-regulated and 92 were up-regulated. Gene Ontology (GO) enrichment analysis indicated that the DEGs were significantly enriched in some tumor-associated biological processes, molecular functions, and cellular components. Kyoto Encyclopedia Genes and Genomes (KEGG) pathway enrichment analysis resulted in 5 cancer related pathways. A total of 10 hub genes were identified based on the topological analysis of PPI network. Survival analysis showed 7 of the hub genes were associated with significantly decreased survival time of the ovarian cancer patients (P<0.05). Conclusion: Our study resulted in identification of 7 hub genes contributing to the development of ovarian cancer. These hub genes may be potentially therapeutic target genes for treatment of ovarian cancer.