Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis

生物 转录组 背景(考古学) 基因 计算生物学 表型 基因表达 遗传学 古生物学
作者
Junyu Long,Shan Huang,Yi Bai,Jinzhu Mao,Anqiang Wang,Yu Lin,Xu Yang,Dongxu Wang,Jianzhen Lin,Jin‐Song Bian,Xiaobo Yang,Xinting Sang,Xi Wang,Haitao Zhao
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:22 (4) 被引量:51
标识
DOI:10.1093/bib/bbaa224
摘要

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.
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