<p>Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA</p>
Zelin Tian, Weixiang He, Jianing Tang, Xing Liao, Qian Yang, Yumin Wu, Gaosong Wu
Abstract
Introduction: Breast cancer (BRCA) has the highest incidence among female malignancies, and the prognosis for these patients remains poor. Materials and Methods: In this study, core modules and central genes related to BRCA were identified through a weighted gene co-expression network analysis (WGCNA). Gene expression profiles and clinical data of GSE25066 were obtained from the Gene Expression Omnibus (GEO) database. The result was validated with RNA-seq data from The Cancer Genome Atlas (TCGA) and Oncomine database. The top 30 key module genes with the highest intramodule connectivity were selected as the core genes (R 2 = 0.40). Results: According to TCGA and Oncomine datasets, seven genes were selected as candidate hub genes. Following further experimental verification, four hub genes (FAM171A1, NDFIP1, SKP1, and REEP5) were retained. Conclusion: We identified four hub genes as candidate biomarkers for BRCA. These hub genes may provide a theoretical basis for targeted therapy against BRCA. Keywords: breast cancer, WGCNA, GEO, Oncomine, prognosis