title: Prognostic gene signature identification using causal structure learning: applications in kidney cancer.
abstract: 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.
authors: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.
authors: Min Jin
authors: MJ
authors: Ha
authors: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.
authors: Veerabhadran
authors: V
authors: Baladandayuthapani
authors: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.
authors: Kim-Anh
authors: KA
authors: Do
keywords: Gaussian graphical models; Markov equivalence class; Peter and Clark (PC) algorithm; kidney cancer; network; survival time