膜性肾病关键基因的生物信息学分析

Bioinformatics analysis of key genes involved in MN

目的基于微阵列数据鉴定膜性肾病(MN)中显著差异表达的基因,并为膜性肾病的诊断与治疗寻找新的潜在基因靶点。方法通过基因表达数据库(GEO)下载GSE108109、GSE115857两个数据集。应用GEO2R工具筛选出正常肾组织与MN患者肾组织的差异表达基因(DEGs)。应用DAVID数据库,对DEGs进行基因本体论(GO)富集和京都基因与基因组百科全书(KEGG)通路富集。然后,应用STRING工具和Cytoscape软件构建并可视化DEGs的蛋白质—蛋白质相互作用(PPI)网络,通过Degree分析识别其中的Hub基因。应用NephonSeqv5在线平台分析Hub基因表达与MN临床特征的关系。结果共筛选出240个DEGs,其中表达上调基因149个,表达下调基因91个。DEGs涉及许多功能和表达途径,如细胞形态调节及细胞骨架的构建。在PPI网络中,共识别出13个Hub基因,包括TP53、PIK3R1、KIT、QSOX1、LAMB1、SHC1、CDK2、APOL1、NES、PTPN6、POLR2A、PRKACA、LTBP1。PRKACA、LTBP1基因表达与蛋白尿呈正相关(r分别为0.858、0.799,P分别为0.006、0.017),POLR2A的表达与蛋白尿呈负相关(r=-0.866,P=0.005)。PRKACA基因表达与血肌酐呈负相关(r=-0.601,P=0.008),POLR2A的表达与血肌酐呈正相关(r=0.583,P=0.011)。结论通过生物信息学分析MN肾组织微阵列数据,发现240个DEGs和13个Hub基因,这些基因可能为MN的诊断与治疗提供潜在基因靶点。

Objective To identify differentially expressed genes(DEGs) in membranous nephropathy(MN) based on microarray data,and to find new potential gene targets for the diagnosis and treatment of MN.Methods The GSE108109 and GSE115857 datasets were downloaded from the Gene Expression Omnibus(GEO) database.The DEGs between healthy controls and MN patients were screened by the GEO2R web tool.By using the David database,DEGs were enriched by Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway.Then,the proteinprotein interaction(PPI) network of DEGs was constructed and visualized by using the String tool and Cytoscape software,and key genes were identified by the Degree analysis.The relationship between expression of key genes and clinical features of MN was analyzed by using nephonseq V5 online platform.Results A total of 240 DEGs were screened out,of which 149 were up-regulated and 91 were down-regulated.DEGs involved many functions and expression pathways,such as cell morphology regulation and cytoskeleton construction.In PPI network,13 key genes were identified,included tumor protein p53(TP53),phosphoinositide-3-kinase regulatory subunit 1(PIK3R1),KIT proto-oncogene,receptor tyrosine kinase(KIT),quiescin sulfhydryl oxidase 1(QSOX1),laminin subunit beta 1(LAMB1),SHC adaptor protein 1(SHC1),cyclin dependent kinase 2(CDK2),apolipoprotein L1(APOL1),nestin(NES),protein tyrosine phosphatase non-recepfor type 6(PTPN6),RNA polymerase Ⅱ subunit A(POLR2 A),protein kinase cAMP-activated catalytic subunit alpha(PRKACA) and latent transforming growth factor beta binding protein 1(LTBP1).Among them,the expression of PRKACA(r=0.858,P=0.006) and LTBP1(r=0.799,P=0.017) was positively correlated with urine protein,and the expression of POLR2A was negatively correlated with urine protein(r=-0.601,P=0.008).PRKACA gene expression was negatively correlated with serum creatinine,and POLR2A expression was positively correlated with serum creatinine(r=0.583,P=0.011).Conclusions In this study,we find 240 DEGs and 13 key genes by bioinformatics analysis of renal tissue microarray data.These genes may provide potential gene targets for the diagnosis and treatment of MN.