Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38849409", "kind": "science-record", "revision": 4, "content_hash": "sha256:cb46b6b0586ebcd3ec53f59264fc630330db4f46d0e1c5d88a8ad46a01725ab0", "created_at": "2026-11-01T19:20:47Z", "updated_at": "2026-11-01T22:12:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.", "doi": "10.1038/s41598-024-63891-2", "pmid": "38849409", "authors": ["Yang Xie", "Jun Li", "Qing Tao", "Yonghui Wu", "Zide Liu", "Youxiang Chen", "Chunyan Zeng"], "journal": "Scientific reports", "year": 2024, "abstract": "Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.", "claims": [{"id": "claim-method", "statement": "We used the Wilcoxon rank-sum test to assess for significant differences between the two groups, with a p-value < 0.05 considered statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38849409", "locator": "sections[16] \"Statistical analysis\" paragraph 2", "quote": "We used the Wilcoxon rank-sum test to assess for significant differences between the two groups, with a p-value < 0.05 considered statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that endorse the results of this research are available upon reasonable request from the corresponding authors (CYZ and YXC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38849409", "locator": "sections[34] \"notes\" paragraph 1", "quote": "The data that endorse the results of this research are available upon reasonable request from the corresponding authors (CYZ and YXC)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the GSE92415 training cohort, CIBERSORT-based immune-cell analysis showed significant subtype-associated differences in intestinal immune-cell infiltration.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38849409", "locator": "sections[23] \"Immune-infiltrating landscape of UC in training cohort\" paragraph 2", "quote": "The difference in abundance of immune cell infiltration in the intestinal mucosa between the C1 and C2 subtypes in UC is depicted in Fig. 6C."}, {"asset": "source:pmid:38849409", "locator": "sections[23] \"Immune-infiltrating landscape of UC in training cohort\" paragraph 2", "quote": "Immune cells with higher levels of infiltration in the intestinal mucosa of patients with C1 UC compared to the C2 group included B cells naïve (p = 0.017), T cells CD4 naïve (p = 0.019), T cells CD4 memory activated (p = 0.004), T cells follicular helper (p < 0.001), M0 macrophages (p < 0.001), M1 macrophages (p = 0.018), activated dendritic cells (p = 0.001), and neutrophils (p < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In the GSE92415 training cohort, CIBERSORT-based immune-cell analysis showed significant subtype-associated differences in intestinal immune-cell infiltration.", "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:38849409"], "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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