Literature DB >> 32387385

Identification of co-expression modules and potential biomarkers of breast cancer by WGCNA.

Ruikang Jia1, Huaxu Zhao1, Mengwen Jia2.   

Abstract

Breast cancer is a very serious disease that threatens human health. The identification of co-expression modules is conducive to revealing the interaction mechanism between genes. The potential biomarkers identified from the co-expression modules have profound implications for the diagnosis and treatment of breast cancer. According to the clinical staging information, the gene expression data of breast cancer was divided into different stages and analyzed separately. The co-expression modules for each stage were identified by WGCNA. The pathways involved in the co-expression modules of each stage were revealed by KEGG enrichment analysis. Combined with clinical information, 81 core genes were screened from the co-expression modules of each stage. By constructing a support vector machine, it was confirmed that these core genes can effectively distinguish breast cancer samples. The biological functions involved in these core genes are revealed by GO enrichment analysis. Survival analysis showed that the expression of 11 genes had significant effects on the survival of breast cancer patients. These results may provide a reference for the mechanism study of breast cancer.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Breast cancer; Co-expression module; Gene expression analysis; Potential biomarker; Support Vector Machines; Weighted gene co-expression network analysis

Year:  2020        PMID: 32387385     DOI: 10.1016/j.gene.2020.144757

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  12 in total

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