Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA

Abstract

Background: As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment. Methods: We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model. Results: A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions. Conclusions: Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.

Keywords

• Renal ischemia‒reperfusion injury
• Random forest
• Support vector machine recursive feature elimination