Integrative network analysis of TCGA data for ovarian cancer

BMC Systems Biology
Qingyang ZhangJi-Ping Wang

Abstract

Over the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interactions of different genetic and epigenetic factors. We propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian 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 logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, C...Continue Reading

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Related Concepts

TP53 gene
ATM wt Allele
Biochemical Pathway
Tumor Suppressor Genes
Protein Methylation
Somatic Mutation
Glycoprotein Biosynthetic Process
Cell Division
Ovarian Neoplasm
Neoplasms

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