Molecular insights into ulcerative colitis and orbital inflammation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40021664", "kind": "science-record", "revision": 3, "content_hash": "sha256:df37d4e0b3acafa847cb63e6268ce179f28d70ce745c9232b8d41ed9d9e2ada9", "created_at": "2026-11-01T20:30:14Z", "updated_at": "2026-11-01T22:13:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Molecular insights into ulcerative colitis and orbital inflammation.", "doi": "10.1038/s41598-025-89344-y", "pmid": "40021664", "authors": ["Kang Tan", "Pei Liu", "Zixuan Wu", "Xi Long", "Yunfeng Yu", "Pengfei Jiang", "Qinghua Peng"], "journal": "Scientific reports", "year": 2025, "abstract": "Ulcerative colitis (UC) is an increasingly prevalent inflammatory condition affecting the intestinal mucosa, while nonspecific orbital inflammation (NSOI) is a common non-neoplastic orbital disorder. Exploring the molecular interplay between UC and NSOI may help physicians make earlier diagnoses and enhance treatment approaches. We analyzed gene expression datasets (GSE58331, GSE105149, GSE206285, and GSE179285) for UC and NSOI from the GEO database. Using WGCNA and differential expression analysis, we identified genes commonly altered in both diseases. GO enrichment, PPI networks, and transcription factor prediction were performed using Cytoscape plugins (cytoHubba and iRegulon). Machine learning techniques were employed to assess transcription factor activity and evaluate potential therapeutic targets among the hub genes. We conducted an association analysis using the TwoSampleMR package in R to explore potential causal relationships between NSOI and UC. A total of 85 intersecting genes between NSOI and UC were identified, and enrichment analyses revealed their roles in immune and inflammatory processes. Key biomarkers, including CXCL10, CXCR4, CXCL9, CD27, SELL, MMP9, CD79A, CD3E, GZMK, and CCL19, were highlighted, linking them to processes such as leukocyte migration, viral response, and monocyte differentiation. STAT1 was identified as a shared transcription factor influencing both diseases. Machine learning algorithms identified eight potential genes for diagnostic and therapeutic use, with CXCL10 emerging as a key player in the pathogenesis of NSOI and UC. CXCL10 likely regulates CXCR4, LCK, CCR7, and other genes involved in pathways such as cytokine-cytokine receptor interactions, HIV-1 infection, and Epstein-Barr virus infection. This study offers insights into the co-pathogenic mechanisms of UC and NSOI, providing a foundation for further mechanistic research and therapeutic development.", "claims": [{"id": "claim-method", "statement": "By integrating the results of weighted gene co-expression network analysis (WGCNA) with the DEGs, we constructed a protein–protein interaction (PPI) network, applying a stringent interaction score threshold of 0.7 to ensure the robustness of the identified relationships.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40021664", "locator": "sections[6] \"Identification of hub DEGs\" paragraph 2", "quote": "By integrating the results of weighted gene co-expression network analysis (WGCNA) with the DEGs, we constructed a protein–protein interaction (PPI) network, applying a stringent interaction score threshold of 0.7 to ensure the robustness of the identified relationships."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/, GSE58331, GSE105149, GSE58331, and GSE105149.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40021664", "locator": "sections[27] \"Data availability\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/, GSE58331, GSE105149, GSE58331, and GSE105149."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 85 intersecting genes between NSOI and UC were identified, and enrichment analyses revealed their roles in immune and inflammatory processes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40021664", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 85 intersecting genes between NSOI and UC were identified, and enrichment analyses revealed their roles in immune and inflammatory processes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 85 intersecting genes between NSOI and UC were identified, and enrichment analyses revealed their roles in immune and inflammatory processes.", "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:40021664"], "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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