Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

Background: Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process. Results: We sought to examine the stability of prognostic signatures based on gene sets rather than individual genes. We classified breast cancer cases from five microarray studies according to the risk of metastasis, using features derived from predefined gene sets. The expression levels of genes in the sets are aggregated, using what we call a set statistic. The resulting prognostic gene sets were as predictive as the lists of individual genes, but displayed more consistent rankings vi...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Gad Abraham, Adam Kowalczyk, Sherene Loi, Izhak Haviv, Justin Zobel
• Keywords: BMCBI 2010, Gene, Gene Sets, Individual Genes