The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learning, and unsupervised clustering.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41041634", "kind": "science-record", "revision": 4, "content_hash": "sha256:4bb728d6ca5eb5bd7d5b7f7ff9230300657a7ec07410f6b13875cdc45a343d72", "created_at": "2026-11-01T20:31:06Z", "updated_at": "2026-11-01T22:57:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learning, and unsupervised clustering.", "doi": "10.3389/fphar.2025.1615269", "pmid": "41041634", "authors": ["Qing Lu", "Yujie Wu", "Dayong Liao", "Ying Sun"], "journal": "Frontiers in pharmacology", "year": 2025, "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.\nMethods: 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.\nResults: 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.\nConclusion: 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.", "claims": [{"id": "claim-method", "statement": "We applied the limma package to perform differential expression analysis, identifying differentially expressed genes (DEGs) between healthy controls and sepsis samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41041634", "locator": "sections[4] \"Identification of α-HB-related targets\" paragraph 2", "quote": "We applied the limma package to perform differential expression analysis, identifying differentially expressed genes (DEGs) between healthy controls and sepsis samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41041634", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine learning-based feature selection identified four robust biomarkers (APEX1, CTSD, SLC40A1, PIK3CB) associated with sepsis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41041634", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Machine learning-based feature selection identified four robust biomarkers (APEX1, CTSD, SLC40A1, PIK3CB) associated with sepsis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Machine learning-based feature selection identified four robust biomarkers (APEX1, CTSD, SLC40A1, PIK3CB) associated with sepsis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41041634"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13904"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41041634", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2025.1615269", "sha256": 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