The Predictive Value of Tumor Necrosis Factor Receptor-Associated Factor-Interacting Protein With Forkhead-Associated Domain (TIFA) and Interleukin-1 Beta in Sepsis-Associated Acute Kidney Injury: Bioinformatics Analysis and Experimental Validation

Background and objective Sepsis is a systemic inflammatory response syndrome caused by severe infection. Sepsis-associated acute kidney injury (SA-AKI) is one of the most common complications of sepsis. Early prediction and subsequent treatment of SA-AKI can improve patient outcomes; hence, the accurate prediction of its occurrence is of paramount importance. This study aimed to investigate the predictive value of the potential biomarkers interleukin-1 beta (IL-1β) and tumor necrosis factor receptor‑associated factor (TRAF)‑interacting protein with forkhead‑associated domain (TIFA) related to the development of SA-AKI. Methods We identified relevant GSE datasets (225192) from the Gene Expression Omnibus (GEO) database and conducted secondary analyses, revealing increased expression of TIFA and IL-1β in renal tissues. Building on our preliminary findings, we performed a prospective observational study (March 2024 to December 2024) among patients with sepsis who were admitted to the Department of Critical Care Medicine at the First Affiliated Hospital of Xinjiang Medical University. Patients were stratified based on the development of AKI. Plasma samples were collected within 24 hours of ICU admission and analyzed using enzyme-linked immunosorbent assay (ELISA) to measure plasma levels of TIFA and IL-1β. Results The analysis revealed that the length of hospital stay, albumin/globulin ratio, and white blood cell count did not show any significant differences between groups. However, plasma levels of TIFA and IL-1β were significantly higher in patients with AKI compared to those without AKI. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.912, indicating that TIFA and IL-1β possess high discriminatory power and calibration accuracy. These findings suggest that plasma levels of TIFA and IL-1β are closely associated with respect to the prediction of AKI in patients. Conclusions Bioinformatics analysis and experimental validation revealed that the expression levels of TIFA and IL-1β are significantly upregulated in patients with SA-AKI. These findings suggest that TIFA and IL-1β may serve as potential biomarkers for predicting SA-AKI.