Exploring the Mechanisms of the Ferroptosis-Related Gene TGFBR1 in Autoimmune Uveitis Based on Machine Learning Models.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40657117", "kind": "science-record", "revision": 4, "content_hash": "sha256:dbaf770ac617e0b697ceeb0b62151a36535213920d0d61d9dfe2dc7f8edab60c", "created_at": "2026-11-01T20:30:45Z", "updated_at": "2026-11-01T22:56:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the Mechanisms of the Ferroptosis-Related Gene TGFBR1 in Autoimmune Uveitis Based on Machine Learning Models.", "doi": "10.1021/acsomega.5c00595", "pmid": "40657117", "authors": ["Danlei Zhang", "He Ren", "Yiqiao Xing", "Zhen Chen"], "journal": "ACS omega", "year": 2025, "abstract": "Autoimmune uveitis (AU) is a group of autoimmune-driven diseases characterized by intraocular inflammation, often leading to severe vision loss. Ferroptosis, a recently discovered form of programmed cell death, has not yet been fully explored in the pathogenesis of AU. This study aims to investigate the role of ferroptosis-related key genes in AU, providing a theoretical foundation for further mechanistic studies. We downloaded GSE198533 data set from the Gene Expression Omnibus (GEO). Through differential gene expression (DEG) analysis, weighted gene coexpression network analysis (WGCNA), and two machine learning models, TGFBR1 and ZFAS1 were identified as critical hub genes. Additionally, we validated the expression of TGFBR1 in retinal inflammation in a mouse model of experimental autoimmune uveitis (EAU) and explored its functional role. The results showed that TGFBR1 expression was significantly downregulated in EAU. Functional experiments demonstrated that TGFBR1 overexpression exacerbated retinal inflammation by promoting lipid peroxidation, downregulating GPX4 and xCT, and altering immune homeostasis by suppressing FOXP3 and enhancing IL-17A expression. Conversely, TGFBR1 inhibition via Galunisertib alleviated retinal inflammation and reversed ferroptosis- and immunity-related protein expression. These findings suggest that TGFBR1 contributes to AU pathogenesis by linking ferroptosis and immune imbalance and may serve as a potential biomarker and therapeutic target, particularly in BD-associated uveitis.", "claims": [{"id": "claim-method", "statement": "To identify optimal feature genes among cDEGs, we applied two supervised machine learning techniques: LASSO logistic regression and SVM-RFE.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40657117", "locator": "sections[19] \"Screening Hub Genes by Machine Learning\" paragraph 2", "quote": "To identify optimal feature genes among cDEGs, we applied two supervised machine learning techniques: LASSO logistic regression and SVM-RFE."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In this study, we utilized the GSE198533 data set, which comprises sample data from 9 patients with BD and 10 healthy controls, in conjunction with ferroptosis-related genes from the Ferrdb database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40657117", "locator": "sections[3] \"Weighted Gene Co-Expression Network Construction and Screening of Differentially Expressed Ferroptosis-Related Genes\" paragraph 2", "quote": "In this study, we utilized the GSE198533 data set, which comprises sample data from 9 patients with BD and 10 healthy controls, in conjunction with ferroptosis-related genes from the Ferrdb database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through differential gene expression (DEG) analysis, weighted gene coexpression network analysis (WGCNA), and two machine learning models, TGFBR1 and ZFAS1 were identified as critical hub genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40657117", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We downloaded GSE198533 data set from the Gene Expression Omnibus (GEO)."}, {"asset": "source:pmid:40657117", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Through differential gene expression (DEG) analysis, weighted gene coexpression network analysis (WGCNA), and two machine learning models, TGFBR1 and ZFAS1 were identified as critical hub genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through differential gene expression (DEG) analysis, weighted gene coexpression network analysis (WGCNA), and two machine learning models, TGFBR1 and ZFAS1 were identified as critical hub genes.", "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:40657117"], "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. 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