Identification of Potential Gene and MicroRNA Biomarkers of Acute Kidney Injury

Abstract

Acute kidney injury (AKI) is a disease that seriously endangers human health. At present, AKI lacks effective treatment methods, so it is particularly important to find effective treatment measures and targets. Bioinformatics analysis has become an important method to identify significant processes of disease occurrence and development. In this study, we analyzed the public expression profile with bioinformatics analysis to identify differentially expressed genes (DEGs) in two types of common AKI models (ischemia-reperfusion injury and cisplatin). DEGs were predicted in four commonly used microRNA databases, and it was found that miR-466 and miR-709 may play important roles in AKI. Then, we found key nodes through protein-protein interaction (PPI) network analysis and subnetwork analysis. Finally, by detecting the expression levels in the renal tissues of the two established AKI models, we found that Myc, Mcm5, E2f1, Oip5, Mdm2, E2f8, miR-466, and miR-709 may be important genes and miRNAs in the process of AKI damage repair. The findings of our study reveal some candidate genes, miRNAs, and pathways potentially involved in the molecular mechanisms of AKI. These data improve the current understanding of AKI and provide new insight for AKI research and treatment.