Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39414957", "kind": "science-record", "revision": 3, "content_hash": "sha256:3f59bc4089cd69d0bb6e9906fd5576cb01fedbc3cba649829c3dc14adccef06f", "created_at": "2026-11-01T20:29:39Z", "updated_at": "2026-11-01T22:12:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach.", "doi": "10.1038/s41598-024-75797-0", "pmid": "39414957", "authors": ["Lai Jiang", "Shengke Zhang", "Chenglu Jiang", "Haiqing Chen", "Jinbang Huang", "Jinyan Yang", "Hao Chi", "Qibiao Wu", "Guanhu Yang"], "journal": "Scientific reports", "year": 2024, "abstract": "Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features to better understand this autoimmune condition. Methods The sequencing data for both the UC and the control groups were obtained from GEO, including both bulk and single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, and SVM-RFE for biomarker selection. A neural network shaped our diagnostic model, corroborated by GSE92415 as the validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes were screened. Enrichment analysis highlighted roles in complement cascades and cell adhesion. Eight biomarkers were finally identified through multiple machine learning and PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, and CD44. The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group). UC tissues revealed heightened plasma cells, CD8 T cells, and other immune cells. Two unique UC immune patterns emerged, with certain T and NK cells central to immune modulation. Conclusion We identified eight biomarkers of UC by various methods, constructed a diagnostic model through neural networks, and explored the immune complexity of the disease, which contributes to the diagnosis and treatment of UC.", "claims": [{"id": "claim-method", "statement": "The ROC curve of the neural network model was generated using the training datasets (GSE87466) and test datasets (GSE92415), and the performance of the model was comprehensively evaluated by calculating the AUC to assess its overall predictive accuracy", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39414957", "locator": "sections[19] \"Modeling and testing of diagnostic performance using neural network and generation of diagnostic column line graph\" paragraph 2", "quote": "The ROC curve of the neural network model was generated using the training datasets (GSE87466) and test datasets (GSE92415), and the performance of the model was comprehensively evaluated by calculating the AUC to assess its overall predictive accuracy"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). All raw data, codes are available at https://www.jianguoyun.com/p/DXpSfhUQ85jYCxj-rIkFIAA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39414957", "locator": "sections[31] \"notes\" paragraph 1", "quote": "The datasets analyzed in the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). All raw data, codes are available at https://www.jianguoyun.com/p/DXpSfhUQ85jYCxj-rIkFIAA."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39414957", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group).", "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:39414957"], "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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