Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

•1. Genetic Insights into Sepsis-Associated AKI (SA-AKI): Through GWAS analysis, this study unveils the genetic relationship between acute kidney injury (AKI) and sepsis, shedding light on the underlying mechanisms of sepsis-associated AKI (SA-AKI), a critical complication in critically ill patients.•2. Predictive Power of Signature Genes: Employing advanced machine learning algorithms, the study identifies six signature genes with exceptional predictive performance for sepsis, AKI, and SA-AKI. These genes demonstrate near-perfect AUCs in both human datasets and a sepsis mouse model, suggesting their potential as reliable biomarkers.•3. Therapeutic Insights and CeRNA Networks: Beyond diagnosis, the research uncovers 62 potential drug treatments for sepsis and AKI, offering promising pharmacological targets. Additionally, the study constructs ceRNA networks, providing insights into the complex regulatory mechanisms underlying sepsis and AKI pathogenesis. This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients. GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network. T