Identifying the molecular mechanisms of sepsis-associated acute kidney injury and predicting potential drugs

Abstract

Objective: To provide insights into the diagnosis and therapy of SA-AKI via ferroptosis genes. Methods: Based on three datasets (GSE57065, GSE30718, and GSE53771), we used weighted co-expression network analysis to identify the key regulators of SA-AKI, its potential biological functions, and constructed miRNA‒mRNA complex regulatory relationships. We also performed machine learning and in vitro cell experiments to identify ferroptosis genes that are significantly related to SA-AKI in the two datasets. The CIBERSORT algorithm evaluates the degree of infiltration of 22 types of immune cell. We compared the correlation between ferroptosis and immune cells by Pearson’s correlation analysis and verified the key genes related to the immune response to reveal potential diagnostic markers. Finally, we predicted the effects of drugs and the potential therapeutic targets for septic kidney injury by pRRophetic. Results: We found 264 coDEGs involving 1800 miRNA molecules that corresponded to 210 coDEGs. The miRNA‒mRNA ceRNA interaction network was constructed to obtain the top-10 hub nodes. We obtained the top-20 ferroptosis genes, 11 of which were in the intersection. We also identified a relationship between ferroptosis genes and the immune cells in the AKI dataset, which showed that neutrophils were activated and that regulatory T cells were surpassed. Finally, we identified EHT1864 and salubrinal as potential therapeutic agents. Conclusion: This study demonstrated the roles of miR-650 and miR-296-3p genes in SA-AKI. Furthermore, we identified OLFM4, CLU, RRM2, SLC2A3, CCL5, ADAMTS1, and EPHX2 as potential biomarkers. The irregular immune response mediated by neutrophils and Treg cells is involved in the development of AKI and shows a correlation with ferroptosis genes. EHT 1864 and salubrinal have potential as drug candidates in patients with septic acute kidney injury.