膜性肾病关键基因的生物信息学分析.

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-receptor type 6 (PTPN6), RNA polymerase II subunit A (POLR2A), 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. [ABSTRACT FROM AUTHOR]