A pathway-based classification of breast cancer integrating data on differentially expressed genes, copy number variations and microRNA target genes.

ABSTRACT: Breast cancer is a clinically heterogeneous disease characterized by distinct molecular aberrations. Understanding the heterogeneity and identifying subgroups of breast cancer are essential to improving diagnoses and predicting therapeutic responses. In this paper, we propose a classification scheme for breast cancer which integrates data on differentially expressed genes (DEGs), copy number variations (CNVs) and microRNAs (miRNAs)-regulated mRNAs. Pathway information based on the estimation of molecular pathway activity is also applied as a postprocessor to optimize the classifier. A total of 250 malignant breast tumors were analyzed by k-means clustering based on the patterns of the expression profiles of 215 intrinsic genes, and the classification performances were compared with existin

SUBMITTER: Eo HS 

PROVIDER: S-EPMC3887768 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature