Autophagy-related molecular clusters identified as indicators for distinguishing active and latent TB infection in pediatric patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38890657", "kind": "science-record", "revision": 4, "content_hash": "sha256:2558aee6f5f6430d781a84a047ac41bbe79c87e09746e061d83cd5affbd56fbb", "created_at": "2026-11-01T20:28:57Z", "updated_at": "2026-11-01T22:54:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Autophagy-related molecular clusters identified as indicators for distinguishing active and latent TB infection in pediatric patients.", "doi": "10.1186/s12887-024-04881-1", "pmid": "38890657", "authors": ["Yang Yu", "Jie Hua", "Liang Chen"], "journal": "BMC pediatrics", "year": 2024, "abstract": "BACKGROUND: Autophagy is crucial for controlling the manifestation of tuberculosis. This study intends to discover autophagy-related molecular clusters as biomarkers for discriminating between latent tuberculosis (LTBI) and active tuberculosis (ATB) in children through gene expression profile analysis.\nMETHODS: The expression of autophagy modulators was examined in pediatric patients with LTBI and ATB utilizing public datasets from the Gene Expression Omnibus (GEO) collection (GSE39939 and GSE39940).\nRESULTS: In a training dataset (GSE39939), patients with LTBI and ATB exhibited the expression of autophagy-related genes connected with their active immune responses. Two molecular clusters associated with autophagy were identified. Compared to Cluster 1, Cluster 2 was distinguished through decreased adaptive cellular immune response and enhanced inflammatory activation, according to single-sample gene set enrichment analysis (ssGSEA). Per the study of gene set variation, Cluster 2's differentially expressed genes (DEGs) played a role in synthesizing transfer RNA, DNA repair and recombination, and primary immunodeficiency. The peak variation efficiency, root mean square error, and area under the curve (AUC) (AUC = 0.950) were all lowered in random forest models. Finally, a seven-gene-dependent random forest profile was created utilizing the CD247, MAN1C1, FAM84B, HSZFP36, SLC16A10, DTX3, and SIRT4 genes, which performed well against the validation dataset GSE139940 (AUC = 0.888). The nomogram calibration and decision curves performed well in identifying ATB from LTBI.\nCONCLUSIONS: In summary, according to the present investigation, autophagy and the immunopathology of TB might be correlated. Furthermore, this investigation established a compelling prediction expression profile for measuring autophagy subtype development risks, which might be employed as possible biomarkers in children to differentiate ATB from LTBI.", "claims": [{"id": "claim-method", "statement": "Machine learning models (MLM) consisting of support vector machine (SVM), random forest (RF), and generalized linear models (GLM) were used to evaluate the ARG clusters using the R “caret” package (v. 6.0–94).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38890657", "locator": "sections[8] \"Construction of a predictive model through several machine learning techniques\" paragraph 2", "quote": "Machine learning models (MLM) consisting of support vector machine (SVM), random forest (RF), and generalized linear models (GLM) were used to evaluate the ARG clusters using the R “caret” package (v. 6.0–94)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found in GSE37250 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37250), GSE19491 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19491), GSE28623 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28623).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38890657", "locator": "sections[25] \"notes\" paragraph 1", "quote": "Publicly available datasets were analyzed in this study. These data can be found in GSE37250 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37250), GSE19491 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19491), GSE28623 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28623)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest model achieved the best training-set discrimination, with an AUC of 0.950, and the seven-gene model achieved an AUC of 0.888 in the external validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38890657", "locator": "sections[16] \"Development and evaluation of machine learning models\" paragraph 2", "quote": "In comparison to GLM (AUC = 0.707) and SVM (AUC = 0.933), the RF model’s area AUC peaked at 0.950 (Fig. 7e)."}, {"asset": "source:pmid:38890657", "locator": "sections[16] \"Development and evaluation of machine learning models\" paragraph 3", "quote": "The 7-gene prediction model was then verified against an external validation dataset (GSE39940), with ROC curves indicating a 0.888 AUC value (Fig. 9)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The random forest model achieved the best training-set discrimination, with an AUC of 0.950, and the seven-gene model achieved an AUC of 0.888 in the external 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:38890657"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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