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.
Original language | English (US) |
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Pages (from-to) | 23-35 |
Number of pages | 13 |
Journal | Cancer Informatics |
Volume | 14 |
DOIs | |
State | Published - Jan 27 2015 |
Keywords
- Gaussian graphical models
- Kidney cancer
- Markov equivalence class
- Network
- Peter and Clark (PC) algorithm
- Survival time
ASJC Scopus subject areas
- Oncology
- Cancer Research