Identification and Validation of Oxidative Stress-Related Diagnostic Marker Genes and Immune Landscape in Ulcerative Interstitial Cystitis by Integrating Bioinformatics and Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40497148", "kind": "science-record", "revision": 4, "content_hash": "sha256:a7127de1a03abd886ec4ba3d4d72c3bd5f6cf3fe25baaffc02b88553e2620e49", "created_at": "2026-11-01T20:30:36Z", "updated_at": "2026-11-01T22:56:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of Oxidative Stress-Related Diagnostic Marker Genes and Immune Landscape in Ulcerative Interstitial Cystitis by Integrating Bioinformatics and Machine Learning.", "doi": "10.2147/JIR.S524653", "pmid": "40497148", "authors": ["Chaowei Fu", "Yuwei Zhang", "Yu Zhao", "Shiyu Wang", "Yuhua Zhou", "Jing Lv", "Shengkai Jin", "Fengping Liu", "Ninghan Feng"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Purpose: Interstitial cystitis (IC) is a chronic inflammatory disease with autoimmune associations, particularly in ulcerative IC, a severe and refractory subtype. Oxidative stress plays a crucial role in IC pathogenesis, interacting with inflammation and immune cell infiltration. This study aimed to identify oxidative stress-linked biomarkers and explore their relationship with immune cell infiltration to enhance diagnosis and treatment strategies.\nPatients and Methods: The GSE711783 dataset from GEO was analyzed to identify differentially expressed genes in ulcerative IC. Oxidative stress-related genes were sourced from GeneCards, with hub genes identified via WGCNA and protein-protein interaction networks. Diagnostic markers were refined using machine learning, and a nomogram prediction model was developed. Diagnostic biomarkers were validated in vitro and in vivo, immune infiltration was assessed with CIBERSORT, and potential therapeutic drugs were identified through DSigDB.\nResults: Four diagnostic biomarkers-BMP2, MMP9, CCK, and NOS3-were identified and found to be associated with immune cells, including CD4+ T cells and eosinophils. Decitabine was identified as a potential therapeutic candidate. Experimental validation confirmed the expression of these biomarkers.\nConclusion: This study identifies BMP2, MMP9, CCK, and NOS3 as key biomarkers, offering valuable insights into the diagnosis and treatment of IC.", "claims": [{"id": "claim-method", "statement": "Subsequently, we employed the “limma (version 3.56.2)” package to conduct a comprehensive analysis of gene expression between IC and controls.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40497148", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "Subsequently, we employed the “limma (version 3.56.2)” package to conduct a comprehensive analysis of gene expression between IC and controls."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available from the corresponding author, [NF], upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40497148", "locator": "sections[37] \"Data Sharing Statement\" paragraph 2", "quote": "The data that support the findings of this study are available from the corresponding author, [NF], upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The limma analysis of GSE11783 identified 1,893 differentially expressed genes between ulcerative IC and control samples.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "By applying statistical criteria (adj.P < 0.05 and |logFC| > 1), a total of 1893 DEGs were identified, comprising 823 up-regulated genes and 1070 down-regulated genes (Additional file 1: Table S2)."}, "evidence": [{"asset": "source:pmid:40497148", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "To identify the differentially expressed genes (DEGs) in the GSE11783 dataset, we first normalized the raw expression matrix using R software (version 4.3.3). Subsequently, we employed the “limma (version 3.56.2)” package to conduct a comprehensive analysis of gene expression between IC and controls."}, {"asset": "source:pmid:40497148", "locator": "sections[22] \"Identification of the DEGs of IC\" paragraph 2", "quote": "By applying statistical criteria (adj.P < 0.05 and |logFC| > 1), a total of 1893 DEGs were identified, comprising 823 up-regulated genes and 1070 down-regulated genes (Additional file 1: Table S2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The limma analysis of GSE11783 identified 1,893 differentially expressed genes between ulcerative IC and control samples.", "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:40497148"], "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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