Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

Background Breast cancer is the mostly diagnosed malignance in female worldwide. However, the mechanisms of its pathogenesis remain largely unknown. Methods In this study, we used weighted gene co-expression network analysis (WGCNA) to identify novel biomarkers associated with the prognosis of breast cancer. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Results A total of 5 modules were identified via the average linkage hierarchical clustering. And a module significantly with the pathological grade was screened out. 33 genes with high connectivity in the clinically significant module were identified as hub genes. Among them, CASC5 and RAD51 were negatively associated with the overall survival and disease-specific survival. Similar results were observed in the validation dataset. Protein levels of CACS5 and RAD51 were also significantly higher in tumor tissues compared with normal tissues based on the analysis of the Human Protein Atlas. Convincingly, qRT-PCR analysis of breast cancer tissues and matched paracancerous tissue demonstrated that CACS5 and RAD51 were significantly upregulated in in breast cancer compared to paracancerous tissues. Further cell proliferation assay indicated that CACS5 and RAD51 depletion decreased cell proliferation capability. Conclusion In conclusion, our findings suggested that CASC5 and RAD51 could serve as biomarkers related to the prognosis of breast cancer and may be helpful for revealing pathogenic mechanism and developing further research.

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