Integrative Network Analysis of TCGA Data for Ovarian Cancer

• Activity Number: 136
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Qingyang Zhang*+ and Joanna Burdette and Ji-Ping Wang
• Companies: Northwestern University and University of Illinois at Chicago and Northwestern University
• Keywords: TCGA ; Bayesian Network ; Ovarian Cancer

Abstract

We propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relations among these features using a probabilistic graphical model based on TCGA data. In the feature selection, we first defined a set of seed genes by including 48 tumor suppressors and oncogenes and an addition of 36 cancer-related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation at gene TP53. We built a Bayesian network model with a logistic link function to quantify the causal relations among these features. We discovered a set of 13 hub genes from the predicted network, which may play important roles in driving different biological pathways. Clustering analysis reveals four gene clusters corresponding to different cellular processes including cell division, tumor invasion and mitochondrial system etc. In addition, two genes related to glycoprotein, PSG11 and GALNT10, are found to significantly affect the overall survival time of ovarian cancer patients.