Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37901228", "kind": "science-record", "revision": 5, "content_hash": "sha256:1de85635bd2dbf5ce585d7ac7aa57fcf2a5e41e1b2f11bafdaeb4bcd7731113e", "created_at": "2026-11-01T19:02:46Z", "updated_at": "2026-11-01T22:52:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.", "doi": "10.3389/fimmu.2023.1253833", "pmid": "37901228", "authors": ["GuoHua You", "XueGang Zhao", "JianRong Liu", "Kang Yao", "XiaoMeng Yi", "HaiTian Chen", "XuXia Wei", "YiNong Huang", "XingYe Yang", "YunGuo Lei", "ZhiPeng Lin", "YuFeng He", "MingMing Fan", "YuLing An", "TongYu Lu", "HaiJin Lv", "Xin Sui", "HuiMin Yi"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Objective: Sepsis related injury has gradually become the main cause of death in non-cardiac patients in intensive care units, but the underlying pathological and physiological mechanisms remain unclear. The Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) definition emphasized organ dysfunction caused by infection. Neutrophil extracellular traps (NETs) can cause inflammation and have key roles in sepsis organ failure; however, the role of NETs-related genes in sepsis is unknown. Here, we sought to identify key NETs-related genes associate with sepsis.\nMethods: Datasets GSE65682 and GSE145227, including data from 770 patients with sepsis and 54 healthy controls, were downloaded from the GEO database and split into training and validation sets. Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. A machine learning approach was applied to identify key genes, which were used to construct functional networks. Key genes associated with diagnosis and survival of sepsis were screened out. Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).\nResults: Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms. Of these, CYBB and FCAR were independent predictors of poor survival in patients with sepsis. Administration of SFN significantly alleviated murine lung NETs expression and injury, accompanied by whole blood CYBB mRNA level.\nConclusion: CYBB and FCAR may be reliable biomarkers of survival in patients with sepsis, as well as potential targets for sepsis treatment. SFN significantly alleviated NETs-related organs injury, suggesting the therapeutic potential by targeting CYBB in the future.", "claims": [{"id": "claim-method", "statement": "Lasso and SVM machine learning methods were used to further narrow the scope of NETs-related genes involved in sepsis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37901228", "locator": "sections[24] \"Machine learning\" paragraph 2", "quote": "Lasso and SVM machine learning methods were used to further narrow the scope of NETs-related genes involved in sepsis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37901228", "locator": "sections[33] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37901228", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms.", "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:37901228"], "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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further inquiries can be directed to the corresponding author/s.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4308662", "GSM4308663", "GSM4308664", "GSM4308665", "GSM4308666", "GSM4308667", "GSM4308668", "GSM4308669", "GSM4308670", "GSM4308671", "GSM4308672", "GSM4308673", "GSM4308674", "GSM4308675", "GSM4308676", "GSM4308677", "GSM4308678", "GSM4308679", "GSM4308680", "GSM4308681", "GSM4308682", "GSM4308683"], "sample_count": 22, "group_sizes": null, "group_sizes_basis": null, "platform": "23178", "taxon": "Homo sapiens", "title": "Long noncoding RNA and messenger RNA abnormalities in pediatric sepsis", "resolution_checked_at": "2026-11-01T19:09:46Z", "resolution_check": 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