Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41268541", "kind": "science-record", "revision": 4, "content_hash": "sha256:1c47191d3e011c41ad8babff246fafe08744ce0f7692b4e45984831f33cd9b64", "created_at": "2026-11-01T20:31:18Z", "updated_at": "2026-11-01T22:57:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental validation.", "doi": "10.3389/fimmu.2025.1684937", "pmid": "41268541", "authors": ["Guanglin Xiao", "Zhiling Deng", "Ke Qiu", "Aoyi Li", "Xingyue Yi", "Hong Ren"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Objectives: Although glycolytic reprogramming constitutes a fundamental driver of hepatic fibrosis (HF), its precise mechanistic contributions remain incompletely characterized. This investigation systematically identified molecular signatures of glycolysis-related genes (GRGs) in HF. We further developed a glycolytic activity-based model for HF risk stratification.\nMethods: Integrated analysis of GEO datasets (GSE276114, GSE84044, GSE49541) identified differentially expressed genes (DEGs) associated with HF progression. Integrated weighted gene co-expression network analysis (WGCNA) with six machine learning algorithms to identify core GRGs genes associated with HF progression, and systematically characterized their biological functions and immunoregulatory roles through immune infiltration assessment, functional enrichment, consensus clustering, and single-cell differential state analysis. Glycolytic activity was evaluated in CCl4-induced fibrotic mice and TGF-β-stimulated LX-2 cells. Additionally, the expression of core GRGs was validated using immunohistochemical staining and RT-qPCR.\nResults: Through the intersection of WGCNA, DEGs, and GRGs, machine learning identified six core GRGs: B3GNT3, CHST4, DCN, GPC3, SOX9, and VCAN. Based on the core GRGs, three GRG-based molecular subtypes were defined. Cluster C, with higher expression of the core GRGs, exhibited significantly enhanced immune infiltration, particularly of adaptive immune cells compared to Cluster A and B. Cluster C comprised a mixed landscape of T cells, mast cells, and pro-fibrogenic cells, distinct from the innate immune-dominant profiles of Clusters A and B. Both in vivo and in vitro analyses demonstrated enhanced glycolysis in fibrotic progression, accompanied by consistent upregulation of core GRGs.\nConclusions: Glycolytic reprogramming is a key pathogenic driver in HF progression and associated immune infiltration. Investigating this metabolic-immune dysregulation represents a promising therapeutic focus for progression of HF.", "claims": [{"id": "claim-method", "statement": "Six distinct machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), Neural Network (NNET), and Least Absolute Shrinkage and Selection Operator (LASSO)—were employed to assess gene significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41268541", "locator": "sections[7] \"Development and validation of a diagnostic signature for HF progression using machine learning algorithms\" paragraph 2", "quote": "Six distinct machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), Neural Network (NNET), and Least Absolute Shrinkage and Selection Operator (LASSO)—were employed to assess gene significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: GEO database, https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41268541", "locator": "sections[36] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: GEO database, https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Six glycolysis-related core genes—B3GNT3, CHST4, DCN, GPC3, SOX9, and VCAN—were identified and used to construct a diagnostic model that robustly distinguished hepatic fibrosis progression stages.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41268541", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through the intersection of WGCNA, DEGs, and GRGs, machine learning identified six core GRGs: B3GNT3, CHST4, DCN, GPC3, SOX9, and VCAN."}, {"asset": "source:pmid:41268541", "locator": "sections[35] \"Conclusions\" paragraph 2", "quote": "The glycolysis-derived model robustly distinguished fibrotic progression stages, enabling early identification of high-risk patients and supporting personalized therapeutic stratification."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Six glycolysis-related core genes—B3GNT3, CHST4, DCN, GPC3, SOX9, and VCAN—were identified and used to construct a diagnostic model that robustly distinguished hepatic fibrosis progression stages.", "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:41268541"], "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:GSE130970"], "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:GSE130970"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41268541", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1684937", "sha256": "sha256:6a3e66ceec5aa13e39a872be38651f103ebe3885d532278547a870bb158e40f3", "bytes": 44782, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1684937", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12626809", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE130970", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130970", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE130970", "version": null, "note": "Publicly available datasets were analyzed in this study. 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