Coexpression Module Construction by Weighted Gene Coexpression Network Analysis and Identify Potential Prognostic Markers of Breast Cancer

Background: Breast cancer (BC) is a malignant tumor with the highest morbidity among women, disrupting millions of their lives worldwide each year. However, the molecular mechanisms underlying remain unclear. Methods: The RNA-Sequencing and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene coexpression network analysis (WGCNA). Additionally, coexpressed modules were used to detect their correlation with the clinical traits of BC. Next, nodes of the most significant coexpression modules were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, mRNA-lncRNA coexpression network and survival analyses. Results: In total, 2056 differentially expressed mRNAs (DEmRNAs) and 297 differentially expressed lncRNAs (DElncRNAs) were identified and subjected to WGCNA analysis, and 12 coexpression modules were generated. The top five significant modules (turquoise, green, red, brown, and blue modules) were related to one or more clinical traits of BC. In particular, the turquoise and green modules were chosen for further analysis. Next, by lncRNA-mRNA coexpression analysis of the turquoise and green modules, 12 DEmRNAs and 2 DElncRNAs were identified as hub nodes. The lncRNA-associated mRNAs of the networks were commonly related to several cancer-related pathways. Moreover, these networks also revealed central roles for RP11-389C8.2 and TGFBR2 in the turquoise module and MYLK, KIT, and RP11-394O4.5 in the green module. Furthermore, 16 DEmRNAs and 3 DElncRNAs in these two modules were significantly correlated with the overall survival of BC patients. Conclusions: The authors' study identified some prognostic biomarkers that might play important roles in the development and treatment of BC. In particular, lncRNAs AC016995.3, RP1-193H18.2, and RP11-166D19.1 were novel biomarkers for BC.

Automated summary

This study analyzed the RNA-Sequencing and clinical data of breast cancer patients from TCGA database using the weighted gene coexpression network analysis (WGCNA) method. They identified coexpression modules related to breast cancer and discovered potential prognostic biomarkers.