The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition.

ABSTRACT:

Introduction

Gene expression data derived from clinical cancer specimens provide an opportunity to characterize cancer-specific transcriptional programs. Here, we present an analysis delineating a correlation-based gene expression landscape of breast cancer that identifies modules with strong associations to breast cancer-specific and general tumor biology.

Methods

Modules of highly connected genes were extracted from a gene co-expression network that was constructed based on Pearson correlation, and module activities were then calculated using a pathway activity score. Functional annotations of modules were experimentally validated with an siRNA cell spot microarray system using the KPL-4 breast cancer cell line, and by using gene expression data from functional studies. Mo

SUBMITTER: Fredlund E 

PROVIDER: S-EPMC3680939 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature