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A network module-based method for identifying cancer prognostic signatures

Guanming Wu1* and Lincoln Stein12

Author affiliations

1 Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada

2 Department of Molecular Genetics, University of Toronto, 1 King's College Circle, #4386, Medical Sciences Building, Toronto ON M5S 1A8, Canada

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Citation and License

Genome Biology 2012, 13:R112  doi:10.1186/gb-2012-13-12-r112

Published: 10 December 2012

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 webcite