biostudies-literatureHa MJNCI NIH HHS23-35https://www.ebi.ac.uk/biostudies/studies/S-EPMC4362630biostudies-literatureUnknown14(Suppl 1)Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.Cancer informaticsPrognostic gene signature identification using causal structure learning: applications in kidney cancer.PMC4362630P30 CA016672P50 CA140388R01 CA160736Ha MJDo KABaladandayuthapani VfalsePrognostic gene signature identification using causal structure learning: applications in kidney cancer.Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.2015-01-01T00:00:00Z20152024-11-09T10:57:49.631Z2019-03-27T01:48:19ZS-EPMC43626302586121510.4137/CIN.S14873