Identification of common prognostic gene expression signatures with biological meanings from microarray gene expression datasets.

ABSTRACT: Numerous prognostic gene expression signatures for breast cancer were generated previously with few overlap and limited insight into the biology of the disease. Here we introduce a novel algorithm named SCoR (Survival analysis using Cox proportional hazard regression and Random resampling) to apply random resampling and clustering methods in identifying gene features correlated with time to event data. This is shown to reduce overfitting noises involved in microarray data analysis and discover functional gene sets linked to patient survival. SCoR independently identified a common poor prognostic signature composed of cell proliferation genes from six out of eight breast cancer datasets. Furthermore, a sequential SCoR analysis on highly proliferative breast cancers repeatedly identified T/B

SUBMITTER: Yao J 

PROVIDER: S-EPMC3448701 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature