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pmid:40565608
Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.
Rakesh Arya · Hemlata Shakya · Viplov Kumar Biswas · Gyanendra Kumar · Sumendra Yogarayan · Harish Kumar Shakya · Jong-Joo Kim
Genes · 2025 doi:10.3390/genes16060716
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Experiment
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40565608", "kind": "science-record", "revision": 2, "content_hash": "sha256:562b407e08242918ee52a56884eadc5a357e03d2ff53463aba429e1a461146e2", "created_at": "2026-11-01T20:30:39Z", "updated_at": "2026-11-01T21:35:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.", "doi": "10.3390/genes16060716", "pmid": "40565608", "authors": ["Rakesh Arya", "Hemlata Shakya", "Viplov Kumar Biswas", "Gyanendra Kumar", "Sumendra Yogarayan", "Harish Kumar Shakya", "Jong-Joo Kim"], "journal": "Genes", "year": 2025, "abstract": "BACKGROUND: Tuberculosis (TB) remains a major global public health challenge, and diagnosing it can be difficult due to issues such as distinguishing active TB from latent TB infection (LTBI), as well as the sample collection process, which is often time-consuming and lacks sensitivity and specificity. Ferroptosis is emerging as an important factor in TB pathogenesis; however, its underlying molecular mechanisms are not fully understood. Thus, there is a critical need to establish ferroptosis-related diagnostic biomarkers for tuberculosis (TB).\nMETHODS: This study aimed to identify and validate potential ferroptosis-related genes in TB infection while enhancing clinical diagnostic accuracy through bioinformatics-driven gene identification. The microarray expression profile dataset GSE28623 from the Gene Expression Omnibus (GEO) database was used to identify ferroptosis-related differentially expressed genes (FR-DEGs) associated with TB. Subsequently, these genes were used for immune cell infiltration, Gene Set Enrichment Analysis (GSEA), functional enrichment and correlation analyses. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA) and validated in independent datasets GSE37250, GSE39940, GSE19437, and GSE31348.\nRESULTS: A total of 21 FR-DEGs were identified. Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers. These biomarkers were enriched in immune-response related pathways and were validated. Immune cell infiltration, GSEA, functional enrichment and correlation analyses revealed that multiple immune cell types could be activated by FR-DEGs. Throughout anti-TB therapy, the expression of the four hub gene signatures significantly decreased in patients cured of TB.\nCONCLUSIONS: In conclusion, ferroptosis plays a key role in TB pathogenesis. These four hub gene signatures are linked with TB treatment effectiveness and show promise as biomarkers for differentiating TB from LTBI.", "claims": [{"id": "claim-method", "statement": "Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups. WGCNA was employed to identify highly relevant gene modules associated with TB [29].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40565608", "locator": "sections[7] \"2.5. Differential Gene Expression Analysis Using GEO2R\" paragraph 2", "quote": "Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups."}, {"asset": "source:pmid:40565608", "locator": "sections[10] \"2.8. Weighted Gene Co-Expression Network Analysis (WGCNA)\" paragraph 2", "quote": "WGCNA was employed to identify highly relevant gene modules associated with TB [29]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40565608", "locator": "sections[32] \"notes\" paragraph 1", "quote": "Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40565608", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among them, four hub genes (ACSL1, PARP9, TLR4, and ATG3) were identified as diagnostic biomarkers.", "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:40565608"], "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:GSE19439"], "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:GSE19439"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40565608", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/genes16060716", "sha256": 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"sample_accessions": ["GSM981627", "GSM981628", "GSM981629", "GSM981630", "GSM981631", "GSM981632", "GSM981633", "GSM981634", "GSM981635", "GSM981636", "GSM981637", "GSM981638", "GSM981639", "GSM981640", "GSM981641", "GSM981642", "GSM981643", "GSM981644", "GSM981645", "GSM981646", "GSM981647", "GSM981648", "GSM981649", "GSM981650", "GSM981651", "GSM981652", "GSM981653", "GSM981654", "GSM981655", "GSM981656", "GSM981657", "GSM981658", "GSM981659", "GSM981660", "GSM981661", "GSM981662", "GSM981663", "GSM981664", "GSM981665", "GSM981666", "GSM981667", "GSM981668", "GSM981669", "GSM981670", "GSM981671", "GSM981672", "GSM981673", "GSM981674", "GSM981675", "GSM981676", "GSM981677", "GSM981678", "GSM981679", "GSM981680", "GSM981681", "GSM981682", "GSM981683", "GSM981684", "GSM981685", "GSM981686", "GSM981687", "GSM981688", "GSM981689", "GSM981690", "GSM981691", "GSM981692", "GSM981693", "GSM981694", "GSM981695", "GSM981696", "GSM981697", "GSM981698", "GSM981699", "GSM981700", "GSM981701", 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licence not read", "asset:data:geo:GSE31348 not downloaded", "asset:data:geo:GSE31348 licence not read", "asset:data:geo:GSE37250 not downloaded", "asset:data:geo:GSE37250 licence not read", "asset:data:geo:GSE39940 not downloaded", "asset:data:geo:GSE39940 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "GEO2R and Weighted Gene Co-expression Network Analysis (WGCNA)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "reordered", "quote": "Gene expression analysis was conducted using GEO2R, an NCBI-built web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r, accessed on 18 April 2024) [24], to identify differentially expressed genes (DEGs) between TB and LTBI groups. WGCNA was employed to identify highly relevant gene modules associated with TB [29]."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (GEO2R and Weighted Gene Co-expression Network Analysis (WGCNA)), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE28623", "GSE37250", "GSE39940", "GSE19439", "GSE31348"], "resolved_accessions": ["GSE28623", "GSE37250", "GSE39940", "GSE19439", "GSE31348"], "statements": ["Data Availability StatementThe data presented in this study using the GSE28623, GSE37250, GSE39940, GSE19439, and GSE31348 datasets are publicly available in the NCBI Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo, accessed on 18 April 2024)."], "basis": "The paper names GSE28623, GSE37250, GSE39940, GSE19439, GSE31348 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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