Identification of hub genes associated with acute kidney injury induced by renal ischemia–reperfusion injury in mice - He, Sheng; He, Lili; Yan, Fangran; Li, Junda; Liao, Xiaoting; Ling, Maoyao; Jing, Ren; Pan, Linghui (Frontiers in Physiology, 2022)
Background: Acute kidney injury (AKI) is a severe clinical syndrome, and one of the important causes of AKI is ischemia-reperfusion injury. The aim of present study was to investigate the related genes and pathways in the mouse model of AKI induced by ischemia-reperfusion injury (IRI-AKI). Method: Two public databas originated from the NCBI GEO database (GSE62732 and GSE121190) were analyzed using the limma package of R software, and differently expressed genes were be identified (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and Gene set enrichment analysis (GSEA) were performed based on DEGs. Furthermore, protein-protein interaction (PPI) network was constructed to investigate hub-genes, and transcription factor (TF)-hub genes and miRNA-hub genes network were constructed. Finally, drugs and molecular compounds that could interact with hub genes were predicted by DGIdb database. Result: A total of 323 common DEGs were identified in renal IRI group compared to control group. Among these, 260DEGs were upregulated and 66 DEGs were downregulated. These common DEGs were enriched positive regulation of cytokine Production, muscle tissue development and other biological processes by GO enrichment analysis and KEGG analysis indicated them were involved MAPK signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, Apoptosis and Epstein-Barr virus Infection signaling pathway. The results of PPI analysis showed 10 hub genes, including Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1 and Ddit3. Through the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drug or molecular compounds might interact with hub genes. Conclusion: Our findings may provide novel potential biomarkers and insight pathogenesis of IRI-AKI through comprehensive analysis of GEO data, which may suggest reliable basis for early diagnosis and treatment of IRI-AKI.