摘要
目的:利用生物信息学方法寻找丙型肝炎(HCV)和慢性肾脏病(CKD)的共表达差异基因(co-DEGs),并预测HCV相关性肾病潜在的生物标志。方法:在公共基因表达数据库GEO中下载HCV基因芯片数据GSE20948和CKD基因芯片数据GSE15072,利用R语言筛选出各自的差异基因,并于Genecards数据库取CKD与HCV差异基因的取交集,得到co-DEGs,随后通过R语言对两组数据的DEGs进行GO富集分析和KEGG通路分析,并对4个co-DEGs行KEGG通路分析。STRING和Cytoscape构建差异基因表达蛋白的互作网络并筛选关键节点基因。通过AmiGO数据库对co-DEGs进行功能条目注释,并使用CTD数据库搜索co-DEGs可能导致的疾病。最后,进一步通过miRDB、mirDIP、TargetScan数据库筛选co-DEGs靶向的miRNA,使用miEAA数据库分析靶向miRNA的功能通路。结果:GSE20948共筛选出122个HCV的差异基因,GSE15072共筛选出235个CKD的差异基因,与Genecards数据库取交集后得到4个HCV和CKD的co-DEGs:GLRX、NFIL3、PFKFB3和KLF10,GO富集分析显示HCV的DEGs主要参与的分子生物学过程包括胺代谢过程、内源性细胞凋亡信号通路等;CKD的DEGs主要参与的分子生物学过程包括核转录mRNA分解代谢过程及病毒转录等。KEGG通路分析显示HCV的DEGs主要参与抗生素的生物合成通路及氨基酸代谢等通路;CKD的DEGs主要参与氧化磷酸化及非酒精性脂肪性肝病(NAFLD)等通路。KEGG通路分析提示co-DEGs在果糖和甘露糖代谢通路中富集。通过筛选3个miRNA数据库共得到10个co-DEGs靶向的miRNA,其参与的功能通路主要与炎症反应有关。结论:共表达基因GLRX、NFIL3、PFKFB3和KLF10通过自身功能调控多条信号通路影响HCV相关肾病的发生和发展,是其潜在的生物标志。
Objective:Bioinformatics was used to identify co-expressed differentially expressed genes(co-DEGs)of hepatitis c(HCV)and chronic kidney disease(CKD),and potential biomarkers for HCV-related nephropathy was predicted.Methods:HCV gene chip data GSE20948 and CKD gene chip data GSE15072 were downloaded from the public gene expression database GEO,and differential genes were screened by R language,DEGs of CKD and HCV in Genecards database were taken together to obtain co-DEGs,followed by GO analysis and KEGG pathway analysis of DEGs of the two sets of data by R language,and analyzed KEGG pathway of the four co-DEGs.STRING and Cytoscape were used to construct protein interaction network of differential gene expression proteins and screen key node genes.Functional entries of the co-DEGs were annotated by the AmiGO database and CTD database was used to search for diseases that may be caused by co-DEGs.Finally,co-DEGs-targeted miRNAs were further screened by miRDB,mirDIP,and TargetScan databases,and the functional pathways targeting miRNAs were analyzed by miEAA database.Results:GSE20948 screened 122 DEGs of HCV,GSE15072 screened 235 DEGs of CKD,after taking the intersection with Genecards database,obtained 4 co-DEGs:GLRX,NFIL3,PFKFB3 and KLF10.GO enrichment analysis showed that HCV DEGs were mainly involved in molecular biology of process include amine metabolic processes,and endogenous cell apoptosis signaling pathways;the main molecular biological processes that CKD DEGs is involved in include nuclear transcription mRNA catabolism and virus transcription,etc.KEGG pathway analysis showed that HCV DEGs were mainly involved in antibiotic biosynthesis pathway,amino acid metabolism and other pathways;DEGs of CKD were mainly involved in oxidative phosphorylation,non-alcoholic fatty liver disease(NAFLD)and other pathways.KEGG pathway analysis suggested that co-DEGs was enriched in fructose and mannose metabolic pathways.Ten co-DEGs-targeted miRNAs were obtained by screening three miRNA databases,which the functional pathways
作者
陈志敏
林丹华(指导)
CHEN Zhi-Min;LIN Dan-Hua(Department of Nephrology,the Affiliated Hospital of Putian University,Putian 351100,China)
出处
《中国免疫学杂志》
CAS
CSCD
北大核心
2020年第17期2064-2070,共7页
Chinese Journal of Immunology
基金
莆田学院2017年校内科研立项项目(研究生专项)(2017065)。
