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A network module-based method for identifying cancer prognostic signatures

Authors
Journal
Genome Biology
1465-6906
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
13
Issue
12
Identifiers
DOI: 10.1186/gb-2012-13-12-r112
Keywords
  • Method
Disciplines
  • Biology
  • Computer Science

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

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