Integrative Bioinformatics Identification of Hub Genes in Renal Ischemia-reperfusion Injury: A Multi-dataset Consensus Approach Combining Conventional and Dietary Restriction Models

Abstract

Background: Ischemia reperfusion injury (IRI) involves the cellular damage, dysfunction and cell death subsequent to the reperfusion of previously ischemic tissues. The molecular mechanisms of IRI are not well understood. Methods: We used an integrative bioinformatic analysis to identify key molecular mechanisms involved in IRI by utilizing 7 publically available gene expression datasets from conventional and dietary restriction related IRI. Differential gene expression (DEG) analysis and functional enrichment analysis was performed. Hub IRI related genes were identified using a consensus approach from 4 methods; weighted gene co-expression network analysis, immune infiltration analysis, ANOVA with LASSO regression and cluster analysis. Receiver operating curve analysis and support vector machine analysis were performed to examine prediction accuracy. Differential expression analysis of a miRNA-related dataset was performed and a hub gene-DEmiRNA-lncRNA network analysis was constructed. Results: 34 IRI-related genes were identified. Enriched functions included cellular hormone metabolic process and progesterone metabolic process, regulation of Protein digestion and absorption, 2-Oxocarboxylic acid metabolism and AGE-RAGE signaling pathway. Five consensus hub IRI related hub genes; Hpd, Cyp2d9, Aldh1a2, Pigr, Bcat1 were obtained. ROC and SVM analysis indicated high AUC values for the IRI related hub genes. Cyp2d9 was highly correlated with Hpd and Pigr. Hub gene-DEmiRNA-lncRNA network analysis showed Bcat1 as regulated by multiple DEmiRNAs. Conclusion: Using an integrated bioinformatics analysis approach the molecular mechanisms of IRI were deconstructed and 5 candidate genes very highly relevant to IRI pathogenesis were identified. These findings present valuable directions for future translational research.