RESUMO
Breast cancer is a very serious disease that threatens human health. The identification of co-expression modules is conducive to revealing the interaction mechanism between genes. The potential biomarkers identified from the co-expression modules have profound implications for the diagnosis and treatment of breast cancer. According to the clinical staging information, the gene expression data of breast cancer was divided into different stages and analyzed separately. The co-expression modules for each stage were identified by WGCNA. The pathways involved in the co-expression modules of each stage were revealed by KEGG enrichment analysis. Combined with clinical information, 81 core genes were screened from the co-expression modules of each stage. By constructing a support vector machine, it was confirmed that these core genes can effectively distinguish breast cancer samples. The biological functions involved in these core genes are revealed by GO enrichment analysis. Survival analysis showed that the expression of 11 genes had significant effects on the survival of breast cancer patients. These results may provide a reference for the mechanism study of breast cancer.
