Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39931173", "kind": "science-record", "revision": 4, "content_hash": "sha256:cf3f66f2b6441f40d18c423d23b8a12ba7737d9be46d4850f2d508762784c09c", "created_at": "2026-11-01T20:30:12Z", "updated_at": "2026-11-01T22:56:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker.", "doi": "10.2147/JIR.S504040", "pmid": "39931173", "authors": ["Man Ni", "Weilong Peng", "Xiaoguang Wang", "Jingui Li"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Purpose: An increasing proportion of the aging population has led to a rapid increase in the number of elderly patients with ulcerative colitis (UC). However, the molecular mechanisms by which aging causes UC remain unclear. In this study, we explored the role of aging-related genes (ARGs) in UC pathogenesis and diagnosis prediction.\nMethods: Gene expression data were obtained from four independent datasets (GSE75214, GSE87466, GSE94648, and GSE169568) in the GEO database, and ARGs were derived from multiple public databases. After identifying UC-related ARGs, consistent clustering was performed to screen aging-related molecular subtypes, followed by the exploration of differences in the immune microenvironment and pathways between distinct subtypes. Next, core module genes were screened using WGCNA and then the hub genes were characterized using LASSO and random forest methods. Besides, the associations between hub genes, immune cells, and key pathways were explored. Finally, the expression levels of key genes were determined in a dextran sulfate sodium (DSS)-induced UC mouse model by qRT-PCR.\nResults: UC samples were classified into two subtypes (1 and 2), which displayed significant differences in the immune landscape and JAK/STAT signaling pathways. A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. In addition, these hub genes were closely associated with the infiltration of specific immune cells (such as neutrophils, memory B cells, and M2 macrophages) as well as with the JAK/STAT pathway. Later, experimental validation confirmed that ETS1 expression was markedly increased in a mouse model of UC.\nConclusion: Overall, aging, immune dysregulation, and UC process are closely associated. The identified feature genes, particularly ETS1, could serve as novel diagnostic biomarkers for UC. These findings have the potential to enhance the understanding of the age-related mechanisms of UC.", "claims": [{"id": "claim-method", "statement": "Therefore, we established a multivariate logistic regression model based on these seven biomarkers. The model combining the two variables (ETS1 and IL7R) exhibited satisfactory accuracy and was superior to the other combinations.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39931173", "locator": "sections[19] \"Screening for Diagnostic Biomarkers in UC\" paragraph 2", "quote": "Therefore, we established a multivariate logistic regression model based on these seven biomarkers. The model combining the two variables (ETS1 and IL7R) exhibited satisfactory accuracy and was superior to the other combinations."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39931173", "locator": "sections[25] \"Data Sharing Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The two-gene ETS1–IL7R diagnostic model achieved an AUC of 0.96 in the training set and AUCs of 0.817 and 0.882 in the GSE169568 and GSE94648 validation sets, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39931173", "locator": "sections[19] \"Screening for Diagnostic Biomarkers in UC\" paragraph 2", "quote": "In the training, GSE169568 validation, and GSE94648 validation sets, the AUC values were 0.96, 0.817, and 0.882, respectively (Figure 5F and G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The two-gene ETS1–IL7R diagnostic model achieved an AUC of 0.96 in the training set and AUCs of 0.817 and 0.882 in the GSE169568 and GSE94648 validation sets, respectively.", "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:39931173"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE169568"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"auc": "ed using ROC curves and AUC"}, "inputs": ["data:geo:GSE169568"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39931173", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S504040", "sha256": "sha256:00777432171b7ffbb35f3499ffcd6aa4321965ef79b251192376605cf8a96ad6", "bytes": 51773, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JIR.S504040", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11809410", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE169568", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169568", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE169568", "version": null, "note": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. 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These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. 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These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. These data can be found from GEO database (http://www.ncbi.nlm.nih.gov/geo/), with the following accession numbers: GSE75214, GSE87466, GSE94648, and GSE169568. The original data from the experiments are available upon reasonable request. 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Results: UC samples were classif", "ETS1 expression was markedly increased in a mouse model of UC. Conclusion: Overall, aging, immune dy", "in a dextran sulfate sodium (DSS)-induced UC mouse model by qRT-PCR. Results: UC samples were c", "ETS1 expression was markedly increased in a mouse model of UC. Conclusion: Overall, aging, imm"], "order_basis": "paper_text_order:1135", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Therefore, we established a multivariate logistic regression model based on these seven biomarkers. The model combining the two variables (ETS1 and IL7R) exhibited satisfactory accuracy and was superior to the other combinations."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}