Identification of high-quality cancer prognostic markers and metastasis network modules.

ABSTRACT: Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifies prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identified prognostic gene signature sets for both estrogen receptor (ER) negative (-) and positive (+) subtypes. A combinatorial use of the signatures allowed the stratification of patients into low-, intermediate- and high-risk groups in both the training set and in eight independent testing sets containing 1,375 sam

SUBMITTER: Li J 

PROVIDER: S-EPMC2972666 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature