Identification of RING finger protein-related biomarkers in sepsis based on explainable machine learning: A comprehensive analysis
Abstract
Objective: To systematically identify RING finger protein (RNF) biomarkers with diagnostic value in sepsis and to explore their potential as therapeutic targets.
Methods: Sepsis peripheral blood transcriptome datasets from Gene Expression Omnibus (GEO) were integrated. Sepsis-associated RNF genes were identified via differential expression analysis and weighted gene co-expression network analysis (WGCNA). Feature genes were selected using LASSO, SVM-RFE, and SHAP. Summary-data-based Mendelian randomization (SMR) assessed causality, and diagnostic performance was validated by receiver operating characteristic (ROC) curves in three independent cohorts. Single-cell RNA sequencing resolved cellular localization and perturbations. DrugReflector deep learning predicted repurposable drugs, molecular docking evaluated binding affinity, and PheWAS assessed target safety.
Results: A total of 13 sepsis-related RNF genes were identified, and three feature genes (ZFP36L2, RNF125, RNF175) were further selected. SMR showed that only elevated RNF175 expression significantly increased sepsis risk (OR = 1.267, 95% CI 1.002-1.603, P = 0.048). The diagnostic AUCs of RNF175 were 0.931, 0.776, and 0.835 in the training and two validation cohorts, respectively. Single-cell atlas revealed predominant RNF175 expression in T cells with significant upregulation in sepsis (P < 0.001). Deep learning predicted tadalafil as the top candidate, with a molecular docking binding energy of -9.072 kcal/mol for…
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