Machine Learning-Guided Multi-Omics Integration Identifies UGCG as a Candidate Lipid Metabolic Biomarker and Potential Therapeutic Target in Sepsis.

Background: Sepsis is a highly lethal organ dysfunction syndrome characterized by dysregulation of the host immune response. This study aims to identify robust lipid metabolism-related genes (LMRGs) as candidate diagnostic biomarkers and to elucidate their underlying immunometabolic mechanisms. Methods: We integrated two sepsis transcriptomic cohorts to screen for differentially expressed LMRGs and identified metabolic-immune subtypes through consensus clustering. Subsequently, we constructed diagnostic models using three machine learning algorithms to identify core genes. We further performed single-cell RNA sequencing (scRNA-seq) analysis, AI-driven drug screening, and in vitro experimental validation combined with lipidomics to explore the biological functions of the core targets. Results: GSEA results indicated synergistic overexpression of lipid metabolism and innate immune pathways in sepsis. LMRG-based clustering analysis successfully identified two molecular subtypes, with lipid metabolism activity highly correlated with characteristic patterns of immune infiltration. Machine learning algorithms precisely identified three core genes (ALDH9A1, MBOAT2, and UGCG). The diagnostic model demonstrated excellent diagnostic performance in both the training set (AUC = 1.000) and four external validation sets (AUCs ranging from 0.943 to 0.998). BRD-K525600704 was predicted to reverse the transcriptomic signature of sepsis and exhibited favorable binding affinity for UGCG and MBOAT2. scRNA-seq revealed a profound myeloid skew in the sepsis microenvironment. In cell models, knockdown of UGCG significantly suppressed LPS-induced upregulation of inflammatory cytokines. Lipidomics further showed that UGCG knockdown attenuated pathological ceramide accumulation, suggesting partial restoration of cellular sphingolipid metabolic homeostasis. Conclusion: This study developed a candidate sepsis diagnostic model comprising three LMRGs. Through integrated multi-omics analysis and in vitro validation, our findings suggest that UGCG is closely associated with lipid metabolic remodeling and myeloid-mediated inflammatory responses in sepsis. However, large-scale prospective clinical validation is currently lacking and warrants future investigation.