Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41104131", "kind": "science-record", "revision": 4, "content_hash": "sha256:de7ef47006ff72c9c36d4b89d4cf5c8fa101dc46a713aa96e7fc11c1bce362a4", "created_at": "2026-11-01T20:31:09Z", "updated_at": "2026-11-01T22:57:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.", "doi": "10.3389/fcimb.2025.1591464", "pmid": "41104131", "authors": ["Shoupeng Ding", "Yimei Yang", "Chunxiao Huang", "Yuyang Zhou", "Zihan Cai"], "journal": "Frontiers in cellular and infection microbiology", "year": 2025, "abstract": "Object: NETs constitute a pivotal mechanism in the pathogenesis and progression of TB. Despite their recognized importance, the genetic underpinnings of NETs in TB remain inadequately elucidated. Accordingly, the present study endeavors to delineate the molecular characteristics of NRGs in TB, with the objective of reliably identifying associated molecular clusters and biomarkers.\nMethods: Gene expression profiles were analyzed from integrated datasets retrieved from the GEO database. Differential analysis, WGCNA, and an ensemble of 113 machine learning algorithms were employed to identify the core NETs genes. Subsequently, TB patients were stratified into distinct subtypes based on the expression profiles of these core genes, and the differences in immune infiltration characteristics among the subtypes were systematically compared. Finally, RT-qPCR was utilized to validate the differential expression of the key NETs core genes.\nResults: Analysis of the integrated GSE83456 and GSE54992 datasets yielded 630 DEGs. WGCNA subsequently identified a module comprising 1,252 genes, from which 26 key NETs genes were extracted via intersection with known NRGs. Among the ensemble of 113 machine learning methods, the \"StepgIm[both]+RF\" algorithm demonstrated superior performance, ultimately identifying six core NETs genes. Consensus clustering based on the expression profiles of these core genes stratified patients into two distinct subtypes. Functional enrichment analysis further underscored the predominance of immune-related pathways in subtype B. Moreover, immune infiltration analysis revealed marked differences in immune cell composition between the subtypes, thereby confirming a close association between the core NETs genes and these immunological disparities.\nConclusion: Core NETs genes are pivotal in the pathogenesis and progression of tuberculosis, and they hold significant promise as novel biomarkers for the early diagnosis and targeted treatment of TB.", "claims": [{"id": "claim-method", "statement": "Differential expression analysis between patients with tuberculosis (TB) and healthy controls was conducted using the “limma” package in R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41104131", "locator": "sections[5] \"Differential gene expression selection\" paragraph 2", "quote": "Differential expression analysis between patients with tuberculosis (TB) and healthy controls was conducted using the “limma” package in R."}], "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:41104131", "locator": "sections[29] \"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": "Ultimately, employing this algorithm, six key NETs genes were identified, namely AIM2, TNFSF10, C5, IL15, CD274, and CYBB.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41104131", "locator": "sections[20] \"Selection of key NETs genes\" paragraph 2", "quote": "Ultimately, employing this algorithm, six key NETs genes were identified, namely AIM2, TNFSF10, C5, IL15, CD274, and CYBB."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Ultimately, employing this algorithm, six key NETs genes were identified, namely AIM2, TNFSF10, C5, IL15, CD274, and CYBB.", "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:41104131"], "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:GSE119143"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE119143"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41104131", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcimb.2025.1591464", "sha256": "sha256:68c1f29958e3b6d72e69226b43072c4ca62d2783c710efbfe8ec4b818e7f8abf", "bytes": 53402, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcimb.2025.1591464", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12521227", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE119143", "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=GSE119143", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE119143", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:41104131", "data:geo:GSE119143"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE119143"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:03:21Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13029, "completion_tokens": 2683, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41104131", "type": "paper", "label": "pmid:41104131"}, {"id": "source:pmid:41104131", "type": "manuscript", "label": "PMID 41104131 full text"}, {"id": "data:geo:GSE119143", "type": "dataset", "label": "GEO GSE119143"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41104131", "type": "paper", "label": "pmid:41104131"}, {"id": "source:pmid:41104131", "type": "manuscript", "label": "PMID 41104131 full text"}, {"id": "data:geo:GSE119143", "type": "dataset", "label": "GEO GSE119143"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41104131", "type": "extraction", "used": ["source:pmid:41104131"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE119143", "type": "accession_resolution", "used": ["data:geo:GSE119143"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41104131", "parent_hash": "sha256:bf1aa2592b642c168d81f1ebead530dc9a0c1a7a709f1a189ca3eff78b3814b1", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE119143 licence not read", "asset:data:geo:GSE119143 not downloaded", "asset:data:geo:GSE19444 licence not read", "asset:data:geo:GSE19444 not downloaded", "asset:data:geo:GSE34608 licence not read", "asset:data:geo:GSE34608 not downloaded", "asset:data:geo:GSE54992 licence not read", "asset:data:geo:GSE54992 not downloaded", "asset:data:geo:GSE62525 licence not read", "asset:data:geo:GSE62525 not downloaded", "asset:data:geo:GSE83456 licence not read", "asset:data:geo:GSE83456 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["types were systematically compared. Finally, RT-qPCR was utilized to validate the differential ex", "es were systematically compared. Finally, RT-qPCR was utilized to validate the differential ex", "e the expression of the six NETs core genes, RT-qPCR was conducted on clinical samples from five", "he expression of the six NETs core genes, RT-qPCR was conducted on clinical samples from five"], "order_basis": "paper_text_order:924", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differential expression analysis between patients with tuberculosis (TB) and healthy controls was conducted using the “limma” package in R."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}