MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular drivers remain poorly defined. Dysregulated coagulation is increasingly implicated in SA-AKI pathogenesis through microvascular thrombosis and immune crosstalk, but kidney-specific coagulation biomarkers remain uncharacterized. Methods: Using murine (Mus musculus) transcriptomic datasets (GSE120879, GSE227623) from the NCBI GEO database, we integrated bioinformatics and machine learning to identify coagulation-related genes differentially expressed (DE-CRGs) in SA-AKI. Hub genes were validated via external datasets (GSE142615), qRT-PCR, and immunohistochemistry in a cecal ligation and puncture (CLP) mouse model. Immune infiltration and checkpoint correlations were analyzed using ImmuCellAI. Diagnostic performance was assessed in a clinical cohort (n = 15) via ROC curve. Results: Four hub DE-CRGs-C3, F3, Fgg, and Serping1-were consistently upregulated in murine SA-AKI (qRT-PCR fold-changes: 7.4- to 23.6-fold, P < 0.05). F3 protein expression was confirmed by immunohistochemistry ( P < 0.01). Immune profiling revealed T cell/NK cell infiltration and PD-L1 (CD274) co-expression with all hub genes (r = 0.62-0.78, P < 0.05). Clinically, a multimarker panel (fibrinogen, TAT, C3) achieved an AUC of 0.853 (95% CI: 0.72-0.98) for SA-AKI diagnosis. Conclusion: This study identifies C3, F3, Fgg, and Serping1 as potential novel coagulation-immune biomarkers for SA-AKI, with validated diagnostic utility. These findings bridge the critical knowledge gap between coagulation dysregulation and immune-mediated tubular injury in SA-AKI pathogenesis and provide a translational framework for early detection.