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Updated: Dec 12, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA
Zelin Tian1, Weixiang He2, Jianing Tang1
1Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People's Republic of China.
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 (R2 = 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.
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