Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40948644", "kind": "science-record", "revision": 3, "content_hash": "sha256:642ec8227b089bca322c72702bbfa876f5f42b03e8e6e8a054816c51bce6da74", "created_at": "2026-11-01T20:31:02Z", "updated_at": "2026-11-01T22:14:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.", "doi": "10.3389/fneur.2025.1611776", "pmid": "40948644", "authors": ["Haipeng Zhang", "Ti Wu", "Xinghua Li", "Shuangqing Liu", "Yuanyuan Wang", "Yang Cao"], "journal": "Frontiers in neurology", "year": 2025, "abstract": "Purpose: This study aimed to investigate the diagnostic potential of neutrophil extracellular traps (NETs)-related genes in acute ischemic stroke (AIS) through comprehensive bioinformatics analysis.\nMethods: Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. NETs-related DEGs (NDEGs) were identified by intersecting the DEGs with previously reported NETS-related genes. Functional enrichment of NDEGs was performed using Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Key genes were identified via machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest (RF). A diagnostic model was constructed based on the identified hub genes and validated using an independent dataset (GSE58294). Potential regulatory miRNAs and candidate therapeutic compounds were predicted using the TargetScan and DSigDB databases, respectively.\nResults: The discovery dataset included 73 AIS patients and 24 healthy controls, revealed 551 DEGs (225 upregulated, 326 downregulated). The analysis of ssGSEA revealed notable immune dysregulation in AIS patients, characterized by increased neutrophil infiltration and decreased level of Th17, Th1, and TFH cells. GSEA indicated that DEGs were enriched in neutrophil degranulation and innate immune system. NDEGs were significantly enriched in immune regulation and leukocyte apoptosis (GO) and NETs formation pathway (KEGG). Four hub genes-SRC, TLR8, FCAR, and HIF1A-were identified using LASSO and RF algorithms. A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. Furthermore, three regulatory miRNAs (miR-146a-5p, miR-155-5p, and miR-21-5p) and 23 candidate therapeutic drugs were predicted.\nConclusion: To our knowledge, this represents the first comprehensive investigation of NETs-related gene signatures in AIS patients compared with healthy controls. These findings deepen our understanding of immune cell infiltration and the underlying molecular mechanisms involved in stroke, offering novel insights that may enhance diagnostic accuracy and therapeutic strategies for AIS.", "claims": [{"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:40948644", "locator": "sections[20] \"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": "A diagnostic model based on four hub genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40948644", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A diagnostic model based on four hub genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset.", "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:40948644"], "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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