Dataset Information
Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA.
Ontology highlight
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 (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.
SUBMITTER: Tian Z
PROVIDER: S-EPMC7367932 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA.
OncoTargets and therapy 20200712
<h4>Introduction</h4>Breast cancer (BRCA) has the highest incidence among female malignancies, and the prognosis for these patients remains poor.<h4>Materials and methods</h4>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 ...[more]