Bioinformatics Analysis And Identification Of Genes Related To Renal Fibrosis Caused By Obstruction

Objective:The purpose of this study is to To screen and analyze the key genes and related pathways of renal fibrosis by bioinformatics methods,and to find potential biomarkers and therapeutic drugs,so as to provide theoretical basis for the early diagnosis and treatment of renal fibrosis.Methods:The GSE152250 transcriptome raw data was downloaded from the GEO database,and the R language was used to screen for DEGs with up-regulated co-expression in UUO4 days and UUO10 days compared to the sham-operated group.GO and KEGG enrichment analysis of these DEGs was performed using the DIVID tool,and protein-protein interaction network analysis of DEGs was performed using the STRING online database.The cyto Hubba plug-in in Cytoscape software was used to screen the top10 genes as key genes.The key genes were verified by GSE135327 dataset,and their diagnostic value was evaluated by ROC curve.The effect of key genes on GFR in patients with renal fibrosis was analyzed by Nephroseq database.Finally,the DGIdb database was used to predict potential therapeutic drugs.Results:1.In the GSE152250 transcriptome data,1382 DEGs that were up-regulated by UUO4 days and UUO10 days compared with the sham operation group were screened.2.GO enrichment analysis showed that: BP results showed that DEGs were mainly enriched in immune system processes,inflammatory response,cell adhesion,cell cycle,extracellular matrix organization,etc.The MF results showed that DEGs were mainly enriched in protein binding,extracellular matrix structural constituent,identical protein binding,integrin binding,protein heterodimerization activity,etc.The CC results showed that DEGs were mainly enriched on the cell surface,extracellular matrix,extracellular region,cell membrane,extracellular space,etc.3.The results of KEGG enrichment analysis showed that DEGs were mainly enriched in neutrophil extracellular trap formation,TNF signaling pathway,cytokine-cytokine receptor interaction,complement and coagulation cascades,ECM-receptor interaction,NOD-like receptor signaling pathway and other related pathways.4.By constructing a PPI network,the top 10 genes were screened as key genes,including: PTPRC,TP53,ITGB2,MKi67,FN1,ITGAM,STAT3,JUN,CDK1,TLR2.The key genes were verified by GSE135327 dataset.It was found that the expression levels of PTPRC,ITGB2,MKi67,FN1,ITGAM,JUN,CDK1 and TLR2 in renal fibrosis tissues were significantly higher than those in normal renal tissues.Among them PTPRC,ITGB2,MKi67,FN1,ITGAM and TLR2 have higher diagnostic value and are negatively correlated with GFR in patients with renal fibrosis.5.Drug-gene interaction analysis predicted 49 drugs including adalimumab,mycophenolate mofetil,and resveratrol as potential therapeutic drugs for renal fibrosis.Conclusion:1.A total of 1382 up-regulated genes were identified,among which PTPRC,ITGB2,MKi67,FN1,ITGAM and TLR2 may be biomarkers of renal fibrosis.2.NETs formation,TNF signaling pathway,cytokine-cytokine receptor interaction,complement and coagulation cascades,ECM-receptor interaction,NOD-like receptor signaling pathwaymay be the key pathways in the development of renal fibrosis.3.Adalimumab,Mycophenolate mofetil and Resveratrol may be potential therapeutic drugs for renal fibrosis.