title: A network module-based method for identifying cancer prognostic signatures.
abstract: Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin.
authors.forename: Guanming
authors.initials: G
authors.lastname: Wu
authors.forename: Lincoln
authors.initials: L
authors.lastname: Stein
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000401:mortality; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D001943:Breast Neoplasms / Q000235:genetics / Q000401:mortality; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D053208:Kaplan-Meier Estimate; D009369:Neoplasms / Q000401:mortality*; D010051:Ovarian Neoplasms / Q000235:genetics / Q000401:mortality; D011379:Prognosis; D060066:Protein Interaction Maps; D012984:Software
chemical_list: D014408:Biomarkers, Tumor