Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis. Issue 4 (14th October 2020)

• Record Type: Journal Article
• Authors: Long, Junyu; Huang, Shan; Bai, Yi; Mao, Jinzhu; Wang, Anqiang; Lin, Yu; Yang, Xu; Wang, Dongxu; Lin, Jianzhen; Bian, Jin; Yang, Xiaobo; Sang, Xinting; Wang, Xi; Zhao, Haitao
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 4(2021)
• Publication Date: 2020-10-14
• Subjects: cholangiocarcinoma -- weighted gene coexpression network analysis -- hub gene -- tumor differentiation -- prognosis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa224
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.