Prognostic genes of breast cancer revealed by gene co-expression network analysis.

The aim of the present study was to identify genes that may serve as markers for breast cancer prognosis by constructing a gene co-expression network and mining modules associated with survival. Two gene expression datasets of breast cancer were downloaded from ArrayExpress and genes from these datasets with a coefficient of variation >0.5 were selected and underwent functional enrichment analysis with the Database for Annotation, Visualization and Integration Discovery. Gene co-expression networks were constructed with the WGCNA package in R. Modules were identified from the network via cluster analysis. Cox regression was conducted to analyze survival rates. A total of 2,669 genes were selected, and functional enrichment analysis of them revealed that they were mainly associated with the immune response, cell proliferation, cell differentiation and cell adhesion. Seven modules were identified from the gene co-expression network, one of which was found to be significantly associated with patient survival time. Expression status of 144 genes from this module was used to cluster patient samples into two groups, with a significant difference in survival time revealed between these groups. These genes were involved in the cell cycle and tumor protein p53 signaling pathway. The top 10 hub genes were identified in the module. The findings of the present study could advance the understanding of the molecular pathogenesis of breast cancer.