Identification of biomarkers of renal ischemia-reperfusion injury by bioinformatics analysis and single-cell sequencing analysis combined with in vivo validation

Renal ischemia-reperfusion injury (IRI) is a serious clinical complication of kidney injury. This research dealt with investigating the hub genes and pathways associated with renal IRI. The transcriptome expression dataset of mouse renal ischemia samples (GSE39548) was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were filtered by R software for key genes utilized for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene enrichment analysis (GSEA). The gene co-expression network was developed by WGCNA analysis to screen important modules. Hub genes from the intersection of DEGs and WGCNA were subjected to protein-protein interaction (PPI) network. The biomarkers obtained by SVM-REF and LASSO algorithm were validated by other datasets and subjected to GSEA analysis. The expression of biomarkers in renal IRI was detected by qRT-PCR and subjected to single-cell analysis. A total of 157 DEGs were discovered. Biological function analysis depicted that the DEGs were primarily involved in cytokine-cytokine receptor interaction, as well as the signaling pathways IL-17, MAPK, and TNF. The intersection of DEGs and the genes obtained by WGCNA analysis yielded 149 hubs genes. Based on SVM-REF and LASSO algorithm, cyp1a1 and pdk4 were determined as potential biomarkers in individuals with renal ischemia and showed good diagnostic value. qRT-PCR results depicted that cyp1a1 and pdk4 were significantly up-regulated in renal ischemia mice (P < 0.05). Finally, the single-cell analysis identified the expression of Cyp1a1 and Pdk4 in mice kidney tissue. cyp1a1 and pdk4 were identified to play important roles in renal IRI. This research provides a new perspective and basis for studying the pathogenesis of renal IRI and developing new treatments.