Sepsis-associated acute kidney injury (SA-AKI) is a prevalent and severe complication of sepsis, but its complex pathogenesis remains unclear. This study aims to identify potential biomarkers for SA-AKI by elucidating its molecular mechanisms through bioinformatics methods.Transcriptional data related to SA-AKI were obtained from the Gene Expression Omnibus (GEO) database. We used differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to identify characteristic genes associated with SA-AKI and conducted enrichment analyses. Hub genes were determined using protein-protein interaction (PPI) network analysis and the Least Absolute Shrinkage and Selection Operator (LASSO). Additionally, ROC curves were plotted to assess the diagnostic value of these core genes. Immune cell infiltration was analyzed using the CIBERSORT algorithm, and potential associations between the hub genes and clinicopathological features were explored based on the Nephroseq database. Finally, a murine model of SA-AKI was induced with lipopolysaccharide (LPS) to validate the findings, and mRNA abundance and protein production levels of pivotal genes were confirmed via RT-qPCR, Western blotting, and immunohistochemical methods.We identified 268 characteristic genes associated with SA-AKI that are enriched in immune and inflammation-related pathways. Utilizing machine learning techniques, three key genes were screened: GBP2, PSMB8 and PSMB9. The expression patterns of these three genes were well-validated through animal experiments and databases. Correlation between these genes and clinical indicators was confirmed using the Nephroseq database. Furthermore, immune infiltration analysis provided additional insights into their potential functions.GBP2, PSMB8, and PSMB9 are promising candidate genes for SA-AKI, providing a novel perspective on its pathological mechanisms. Further exploration of the biological roles of these genes in the pathogenesis of SA-AKI is needed in the future.© 2025. The Author(s).