Research Paper Volume 13, Issue 2 pp 2519—2538

Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis

Xin Yin1, , Pei Wang1, , Tianshu Yang1, , Gen Li1, , Xu Teng1, , Wei Huang1, , Hefen Yu1, ,

  • 1 Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China

Received: August 5, 2020       Accepted: October 22, 2020       Published: December 9, 2020
How to Cite

Copyright: © 2020 Yin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.


BRCA: Breast cancer; TCGA: The Cancer Genome Atlas; lncRNAs: Long noncoding RNAs; DElncRNAs: Differentially expressed lncRNAs; DEmRNAs: Differentially expressed mRNAs; WGCNA: Weighted gene co-expression network analysis; PPI: Protein-protein interaction; RBP: RNA binding protein; ER: Estrogen receptor; PR: Progesterone receptor; HER2: Human epidermal growth factor 2; ceRNA: Competing endogenous RNA; TOM: Topological matrix; MEs: Module eigengenes; MM: Module membership; GS: Gene significance; MCODE: Molecular Complex Detection; IHC: Immunohistochemistry; HPA: The Human Protein Atlas.