Identification of O-glycosylation related genes and subtypes in ulcerative colitis based on machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39739658", "kind": "science-record", "revision": 4, "content_hash": "sha256:5c3e10a722a88394cb25bb1c8ec12d688239ac5945f5c19588a03d3f5cc9d02c", "created_at": "2026-11-01T20:30:03Z", "updated_at": "2026-11-01T22:55:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of O-glycosylation related genes and subtypes in ulcerative colitis based on machine learning.", "doi": "10.1371/journal.pone.0311495", "pmid": "39739658", "authors": ["Yue Lu", "Yi Su", "Nan Wang", "Dongyue Li", "Huichao Zhang", "Hongyu Xu"], "journal": "PloS one", "year": 2024, "abstract": "Ulcerative colitis (UC) is an immune-related inflammatory bowel disease, with its underlying mechanisms being a central area of clinical research. O-GlcNAcylation plays a critical role in regulating immunity progression and the occurrence of inflammatory diseases and tumors. Yet, the mechanism of O-GlcNAc-associated colitis remains to be elucidated. To this end, the transcriptional and clinical data of GSE75214 and GSE92415 from the GEO database was hereby examined, and genes MUC1, ADAMTS1, GXYLT2, and SEMA5A were found to be significantly related to O-GlcNAcylation using machine learning methods. Based on the four hub genes, two UC subtypes were built. Notably, subtype B might be prone to developing colitis-associated colorectal cancer (CAC). This study delved into the role of intestinal glycosylation changes, especially the O-GlcNAcylation, and forged a foundation for further research on the occurrence and development of UC. Overall, understanding the role of O-GlcNAcylation in UC could have significant implications for diagnosis and treatment, offering valuable insights into the disease's progression.", "claims": [{"id": "claim-method", "statement": "The genes that met the criteria of the adjusted P-value < 0.05 and |log fold change (FC)| > 1.0 or 0.5 were considered DEGs using the \"limma\" package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39739658", "locator": "sections[5] \"Identification of differential expressed genes\" paragraph 2", "quote": "The genes that met the criteria of the adjusted P-value < 0.05 and |log fold change (FC)| > 1.0 or 0.5 were considered DEGs using the \"limma\" package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE75214 database contains intestinal mucosal biopsies obtained endoscopically from UC patients (n = 97) and healthy controls (n = 11), followed by microarray analysis to assess gene expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39739658", "locator": "sections[3] \"Datasets and sample selection\" paragraph 2", "quote": "The GSE75214 database contains intestinal mucosal biopsies obtained endoscopically from UC patients (n = 97) and healthy controls (n = 11), followed by microarray analysis to assess gene expression."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study analyzed the transcriptional and clinical data of GSE75214 and GSE92415 from GEO and identified MUC1, ADAMTS1, GXYLT2, and SEMA5A as significantly related to O-GlcNAcylation using machine-learning methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39739658", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "To this end, the transcriptional and clinical data of GSE75214 and GSE92415 from the GEO database was hereby examined, and genes MUC1, ADAMTS1, GXYLT2, and SEMA5A were found to be significantly related to O-GlcNAcylation using machine learning methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study analyzed the transcriptional and clinical data of GSE75214 and GSE92415 from GEO and identified MUC1, ADAMTS1, GXYLT2, and SEMA5A as significantly related to O-GlcNAcylation using machine-learning methods.", "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:39739658"], "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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