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

Abstract

Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R2 = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.

Identification of 13 Key Genes Correlated With Progression and Prognosis in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

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Transcripto‐based network analysis reveals a model of gene activation in tongue squamous cell carcinomas

“…And it has been one of the most widely used methods with the rapid development of high‐throughput research methods . This analysis aims to group synergetic expressed genes into a same model according to pairwise correlations between genes, and explore the relationship between these models and clinical phenotypes, which is based on the assumption that the co‐expression genes are likely to be similar in biological functions . Besides, it can be used to analyze the interactions among differentially expressed genes in different modules.…”

Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome

“…Although the number of genes in prognostic signatures had a decreasing trend as a whole [50,51], most of the previous prognostic signatures were often in the form of a union set of dozens or hundreds of genes, or even a network module [6,[52][53][54]. This would considerably weaken the importance of individual genes, which may be one of the reasons why the four network centralities of the single PG were not high.…”