摘要
本研究旨在基于加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)筛选肝纤维化发生及发展过程中的关键基因及潜在机制。从GEO数据库获得肝纤维化患者全基因组芯片数据(GSE84044),采用WGCNA发掘与肝纤维化发生发展相关的重要模块及关键基因,通过GO注释和KEGG富集揭示潜在的关键机制。对肝纤维化患者基因组芯片数据进行WGCNA分析,22876个基因被分为8个模块,其中有两个模块与肝纤维化相关性较大,GO注释和KEGG富集的结果表明,这两个模块主要与细胞外基质和胶原蛋白的产生有关。分析WGCNA关键基因在肝纤维化4个时期的表达差异,结果发现,LIPC、DCN、LUM、COL1A1等10个基因呈现出明显变化趋势。本研究采用WGCNA对肝纤维化患者的基因芯片进行分析,挖掘肝纤维化发生发展过程中的重要基因,为肝纤维化预防与治疗提供生物标志物和潜在靶点的新筛选方向。
This study aims to illustrate the crucial genes and potential mechanism in the occurrence and development of liver fibrosis via weighted gene co-expression network analysis(WGCNA).The whole genome expression data of patients with liver fibrosis(GSE84044)were derived from GEO database.WGCNA was conducted to estimate the crucial models and genes correlated with development of liver fibrosis.GO functional annotations and KEGG pathway enrichment were further applied for the investigation of potential mechanism.A total of 22876 genes were clustered into 8 modules by WGCNA,two modules of which were identified as significantly correlated with liver fibrosis.GO annotation and KEGG enrichment results showed that these two modules were mainly related to the production of extracellular matrix and collagen.Analysis of crucial genes of WGCNA in the four stages of liver fibrosis showed that top ten genes,including LIPC,DCN,LUM and COL1A1,were identified as differentially expressed during the development of liver fibrosis.In this study,WGCNA was used to analyze the gene chip of patients with liver fibrosis,to dig out the important genes in the process of liver fibrosis,and to provide a new screening direction of biomarkers and potential targets for the prevention and treatment of liver fibrosis.
作者
赵红静
陆安静
秦琳
谭道鹏
鲁艳柳
何芋岐
周旭美
张倩茹
Zhao Hongjing;Lu Anjing;Qin Lin;Tan Daopeng;Lu Yanliu;He Yuqi;Zhou Xumei;Zhang Qianru(College of Pharmacy,Zunyi Medical University,Zunyi,563099;Engineering Research Center for Key Technology of Guizhou Dendrobium Chrysanthemum Industry Development in Guizhou Higher Education Institution,Zunyi,563099)
出处
《基因组学与应用生物学》
CAS
CSCD
北大核心
2022年第11期2233-2242,共10页
Genomics and Applied Biology
基金
国家自然科学基金项目(82160740)
贵州省科技厅项目(黔科合支撑[2019]2953
黔科合平台人才[2018]5772-33)共同资助