Systematic module approach identifies altered genes and pathways in four types of ovarian cancer

Abstract

The present study aimed to identify altered genes and pathways associated with four histotypes of ovarian cancer, according to the systematic tracking of dysregulated modules of reweighted protein-protein interaction (PPI) networks. Firstly, the PPI network and gene expression data were initially integrated to infer and reweight normal ovarian and four types of ovarian cancer (endometrioid, serous, mucinous and clear cell carcinoma) PPI networks based on Spearman's correlation coefficient. Secondly, modules in the PPI network were mined using a clique-merging algorithm and the differential modules were identified through maximum weight bipartite matching. Finally, the gene compositions in the altered modules were analyzed, and pathway functional enrichment analyses for disrupted module genes were performed. In five conditional-specific networks, universal alterations in gene cor relat ions were revealed, which leads to the different ial correlation density among disrupted module pairs. The analyses revealed 28, 133, 139 and 33 altered modules in endometrioid, serous, mucinous and clear cell carcinoma, respectively. Gene composition analyses of the disrupted modules revealed five common genes (mitogen-activated protein kinase 1, phosphoinositide 3-kinase-encoding catalytic 110-KDα, AKT serine/threonine kinase 1, cyclin D1 and tumor protein P53) across the four subtypes of ovarian cancer. In addition, pathway enrichment analysis confirmed one common pathway (pathways in cancer), in the four histotypes. This systematic module approach successfully identified altered genes and pathways in the four types of ovarian cancer. The extensive differences of gene correlations result in dysfunctional modules, and the coordinated disruption of these modules contributes to the development and progression of ovarian cancer.