J Cancer 2018; 9(17):3016-3022. doi:10.7150/jca.26133
Identification of Genes and Pathways Involved in Ovarian Epithelial Cancer by Bioinformatics Analysis
1. Department of Gynaecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P.R. China
2. Department of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, Boston, MA, 02118, USA
Zhou Y, Layton O, Hong L. Identification of Genes and Pathways Involved in Ovarian Epithelial Cancer by Bioinformatics Analysis. J Cancer 2018; 9(17):3016-3022. doi:10.7150/jca.26133. Available from http://www.jcancer.org/v09p3016.htm
Ovarian epithelial cancer (OEC) is an often fatal disease with poor prognosis in women with high-stage disease. In contrast, ovarian low malignant potential (LMP) tumors with favorable prognosis behaves as a disease between benign and malignant tumors. The involved genes and pathways between benign-like LMP and aggressive OEC are largely unknown. This study integrated two cohorts profile datasets to investigate the potential key candidate genes and pathways associated with OEC. Gene expression in two datasets (GSE9891 and GSE12172), including 327 OECs and 48 LMP tumors, were analyzed. 559 differentially expressed genes were found to overlap, 251 up-regulated and 308 down-regulated. Subsequently, analysis of gene ontology, signaling pathway enrichment and protein-protein interaction (PPI) network was performed. Gene ontology analysis clustered the up-regulated and down-regulated genes based on significant enrichment. 282 nodes/ differentially expressed genes (DEGs) were identified from DEGs PPI network complex, and two most significant k-clique modules were identified from PPI. In a summary, using integrated bioinformatics analysis, we are able to identify biomarkers potentially significant in the pathogenesis of OEC, which can improve our understanding of the cause and molecular events. These candidate genes and pathways could be used for further confirmation, and lead to better disease diagnose and therapy.
Keywords: ovarian epithelial cancer, low malignant potential tumor, chemokine, neoplasm invasiveness, bioinformatics