Assessment of ferroptosis-associated gene signatures as potential biomarkers for differentiating latent from active tuberculosis in children.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37163321", "kind": "science-record", "revision": 4, "content_hash": "sha256:b739ca79c92d1a55216468fa36295b831faf43208e7bbf2a5107ef8178fcd3df", "created_at": "2026-11-01T19:01:49Z", "updated_at": "2026-11-01T22:51:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Assessment of ferroptosis-associated gene signatures as potential biomarkers for differentiating latent from active tuberculosis in children.", "doi": "10.1099/mgen.0.000997", "pmid": "37163321", "authors": ["Liang Chen", "Jie Hua", "Xiaoting Dai", "Xiaopu He"], "journal": "Microbial genomics", "year": 2023, "abstract": "Ferroptotic cell death is a regulated process that is governed by iron-dependent membrane lipid peroxide accumulation that plays a pathogenic role in several disease-related settings. The use of ferroptosis-related genes (FRGs) to distinguish active tuberculosis (ATB) from latent tuberculosis infection (LTBI) among children, however, remains to be analysed. Tuberculosis-related gene expression data and FRG lists were obtained, respectively, from Gene Expression Omnibus (GEO) and FerrDb. Differentially expressed FRGs (DE-FRGs) detected when comparing samples from paediatric ATB and LTBI patients were explored using appropriate bioinformatics techniques, after which enrichment analyses were performed for these genes and hub genes were identified, with these genes then being used to explore potential drug interactions and construct competing endogenous RNA (ceRNA) networks. The GSE39939 dataset yielded 124 DE-FRGs that were primarily related to responses to oxidative, chemical and extracellular stimulus-associated stress. In total, the LASSO and SVM-RFE algorithms enabled the identification of nine hub genes (MAPK14, EGLN2, IDO1, USP11, SCD, CBS, PARP8, PARP16, CDC25A) that exhibited good diagnostic utility. Functional enrichment analyses of these genes suggested that they may govern ATB transition from LTBI through the control of many pathways, including the immune response, DNA repair, transcription, RNA degradation, and glycan and energy metabolism pathways. The CIBERSORT algorithm suggested that these genes were positively correlated with inflammatory and myeloid cell activity while being negatively correlated with the activity of lymphocytes. A total of 50 candidate drugs targeting 6 hub DE-FRGs were also identified, and a ceRNA network was used to explore the complex interplay among these hub genes. The nine hub FRGs defined in this study may serve as valuable biomarkers differentiating between ATB and LTBI in young patients.", "claims": [{"id": "claim-method", "statement": "The glmnet package was utilized to implement a LASSO algorithm to reduce data dimensionality, with DE-FRGs identified when comparing LTBI and ATB samples being retained for feature selection, and biomarkers of ATB then being selected through this algorithmic approach. The SVM package was additionally utilized to implement a support vector machine recursive feature elimination (SVM-RFE) model with 10-fold cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37163321", "locator": "sections[7] \"Optimal diagnostic hub gene selection\" paragraph 2", "quote": "The glmnet package was utilized to implement a LASSO algorithm to reduce data dimensionality, with DE-FRGs identified when comparing LTBI and ATB samples being retained for feature selection, and biomarkers of ATB then being selected through this algorithmic approach. The SVM package was additionally utilized to implement a support vector machine recursive feature elimination (SVM-RFE) model with 10-fold cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All supporting data, code and protocols have been provided within the article or through supplementary data files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37163321", "locator": "sections[27] \"Footnotes\" paragraph 2", "quote": "All supporting data, code and protocols have been provided within the article or through supplementary data files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After development of a logistic regression model using the R ‘glm’ package, it was found that the AUC value for the combination of the nine hub DE-FRGs was 0.830 in the GSE39940 test dataset (Fig. 4b), thus demonstrating that the model is both accurate and specific for individual genes when distinguishing between ATB and LTBI samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37163321", "locator": "sections[16] \"Identification of key DE-FRGs associated with paediatric ATB and LTBI\" paragraph 4", "quote": "After development of a logistic regression model using the R ‘glm’ package, it was found that the AUC value for the combination of the nine hub DE-FRGs was 0.830 in the GSE39940 test dataset (Fig. 4b), thus demonstrating that the model is both accurate and specific for individual genes when distinguishing between ATB and LTBI samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After development of a logistic regression model using the R ‘glm’ package, it was found that the AUC value for the combination of the nine hub DE-FRGs was 0.830 in the GSE39940 test dataset (Fig. 4b), thus demonstrating that the model is both accurate and specific for individual genes when distinguishing between ATB and LTBI samples.", "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:37163321"], "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:GSE39939"], "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:GSE39939"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37163321", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1099/mgen.0.000997", "sha256": 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"parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The glmnet package was utilized to implement a LASSO algorithm to reduce data dimensionality, with DE-FRGs identified when comparing LTBI and ATB samples being retained for feature selection, and biomarkers of ATB then being selected through this algorithmic approach. The SVM package was additionally utilized to implement a support vector machine recursive feature elimination (SVM-RFE) model with 10-fold cross-validation."}}], "targets": [], "gaps": []}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The SVM package was additionally utilized to implement a support vector machine recursive feature elimination (SVM-RFE) model with 10-fold cross-validation."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}