Abstract
BACKGROUND: Sepsis remains a major cause of mortality and morbidity worldwide. Recent studies suggest that gut microbiota-derived metabolites, such as α-hydroxybutyrate (α-HB), may play a critical role in the progression of sepsis. However, the molecular mechanisms underlying α-HB's involvement in sepsis remain unclear. This study aims to explore the targets of α-HB and their association with sepsis progression using multi-database data mining, machine learning, and unsupervised clustering analyses. METHODS: α-HB-related targets were identified through comprehensive data mining from three databases: SEA, SuperPred, and SwissTargetPrediction. Sepsis-related targets were obtained from the GEO dataset GSE26440, and the intersection of these datasets was analyzed to reveal common targets. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. Additionally, a nomogram was constructed to predict sepsis progression. Clustering, GSVA, and ssGSEA analyses were performed to explore sepsis subtypes. Molecular docking simulations was conducted to investigate interactions between α-HB and key targets. RESULTS: A total of 42 common targets were identified between α-HB and sepsis, with significant enrichment in pathways related to immune response, hypoxia, and cancer. Machine learning-based feature selection identified four robust biomarkers (APEX1, CTSD, SLC40A1, PIK3CB) associated with sepsis. The constructed nomogram demonstrated high predictive accuracy for sepsis risk. Unsupervised clustering revealed two distinct α-HB-related sepsis subtypes with differential immune cell infiltration patterns and pathway activities, particularly involving immune and inflammatory pathways. Subtype 1 was predominantly associated with non-survivors, while Subtype 2 was more frequent among survivors, showing a significant difference in survival status. Molecular docking analysis further indicated potential interactions between α-HB and key targets (APEX1, CTSD, SLC40A1, PIK3CB), providing insights into the molecular mechanisms of α-HB in sepsis. CONCLUSION: This study identifies key α-HB-related targets and biomarkers for sepsis, offering new insights into its pathophysiology. The findings highlight the potential of α-HB in modulating immune responses and suggest that α-HB-related targets could serve as promising therapeutic targets for sepsis management.