Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40620701", "kind": "science-record", "revision": 3, "content_hash": "sha256:0c4102af7f06014a2a386ede263b41a86993e6d63f6a0ba7dc739f161c70a556", "created_at": "2026-11-01T20:30:43Z", "updated_at": "2026-11-01T22:14:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning.", "doi": "10.3389/fgene.2025.1589999", "pmid": "40620701", "authors": ["Jiao Li", "Yupei Liu", "Zhiyi Sun", "Suqi Zeng", "Caisong Zheng"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Background: The incidence of ulcerative colitis (UC) is rapidly increasing worldwide, but existing therapeutics are limited. Neutrophil extracellular traps (NETs), which have been associated with the development of various autoimmune diseases, may serve as a novel therapeutic target for UC treatment.\nMethods: Bioinformatics analysis was performed to investigate UC-related datasets downloaded from the GEO database, including GSE87466, GSE75214, and GSE206285. Differentially expressed genes (DEGs) related to NETs in UC patients and healthy controls were identified using Limma R package and WGCNA, followed by functional enrichment analysis. To identify potential diagnostic biomarkers, we applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model, and Random Forest (RF) algorithm, and constructed Receiver Operating Characteristic (ROC) curves to evaluate accuracy. Additionally, immune infiltration analysis was conducted to identify immune cells potentially involved in the regulation of NETs. Finally, the expression of core genes in patients was validated using Quantitative real-time PCR (qRT-PCR), and potential therapeutic drugs for UC were explored through drug target databases.\nResult: Differential analysis of transcriptomic sequencing data from UC samples identified 29 DEGs related to NETs. Enrichment analysis showed that these genes primarily mediate UC-related damage through biological functions such as leukocyte activation, migration, immune receptor activity, and the IL-17 signaling pathway. Three machine learning algorithms successfully identified core NETs-related genes in UC (IL1B, MMP9 and DYSF). According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Additionally, Immune infiltration analysis revealed that the expression of these core genes was closely associated with neutrophils infiltration and CD4+ memory T cell activation, and negatively associated with M2 macrophage infiltration. qRT-PCR showed that the core genes were significantly overexpressed in UC patients. Gevokizumab, canakinumab and carboxylated glucosamine were predicted as potential therapeutic drugs for UC.\nConclusion: By combining three machine learning algorithms and bioinformatics, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of UC, which may provide valuable insights into the mechanism of NETs in UC and potential related therapies.", "claims": [{"id": "claim-method", "statement": "Subsequently, the ‘pROC’ package was utilized to generate the receiver operating characteristic (ROC) curve, evaluating the ability of the hub genes to distinguish between UC patients and healthy individuals across all datasets (Robin et al., 2011).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40620701", "locator": "sections[8] \"External validation and diagnostic performance of core genes\" paragraph 2", "quote": "Subsequently, the ‘pROC’ package was utilized to generate the receiver operating characteristic (ROC) curve, evaluating the ability of the hub genes to distinguish between UC patients and healthy individuals across all datasets (Robin et al., 2011)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40620701", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC analysis showed that the three core genes IL1B, MMP9, and DYSF each had AUC values greater than 0.9 across all three datasets, indicating exceptional predictive ability for distinguishing UC patients from healthy individuals.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40620701", "locator": "sections[18] \"Validation of hub genes expression and diagnostic performance\" paragraph 2", "quote": "The results demonstrated that the AUC values for the core genes in all three datasets were greater than 0.9, indicating exceptional predictive ability."}, {"asset": "source:pmid:40620701", "locator": "sections[18] \"Validation of hub genes expression and diagnostic performance\" paragraph 2", "quote": "These core genes could therefore serve as key molecular biomarkers for diagnosing UC (Figures 5C–E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "ROC analysis showed that the three core genes IL1B, MMP9, and DYSF each had AUC values greater than 0.9 across all three datasets, indicating exceptional predictive ability for distinguishing UC patients from healthy individuals.", "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:40620701"], "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:GSE206285"], "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:group_comparison_wilcoxon", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE206285"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40620701", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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