Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39622968", "kind": "science-record", "revision": 3, "content_hash": "sha256:6a539a66c7861d34f25621f1fde3e44d5c2a91c5c0e03b4e71e4b4be526b0416", "created_at": "2026-11-01T20:29:53Z", "updated_at": "2026-11-01T22:13:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.", "doi": "10.1038/s41598-024-81790-4", "pmid": "39622968", "authors": ["Shoupeng Ding", "Jinghua Gao", "Chunxiao Huang", "Yuyang Zhou", "Yimei Yang", "Zihan Cai"], "journal": "Scientific reports", "year": 2024, "abstract": "Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms-Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model-to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p < 0.01), and a clinical predictive model was developed based on these critical genes. Patients were stratified into distinct subtypes according to m6A gene expression profiles, followed by immune infiltration analysis across subtypes. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated the biological functions and pathways associated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key m6A regulatory genes. Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes-YTHDF1, HNRNPC, LRPPRC, and ELAVL1-identified as critical m6A regulators in TB through the Random Forest model. The diagnostic significance of these genes was further supported by a nomogram, achieving a high predictive accuracy (95% confidence interval [CI]: 0.87-0.94). Consensus clustering classified patients into two m6A subtypes with distinct immune profiles, as principal component analysis (PCA) showed significantly higher m6A scores in Group A than in Group B (p < 0.05). Immune infiltration analysis highlighted significant correlations between key m6A genes and specific immune cell infiltration patterns across subtypes. This study highlights the potential of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, supporting their role in TB pathogenesis. Future research should aim to further validate these findings across diverse cohorts to enhance their clinical applicability.", "claims": [{"id": "claim-method", "statement": "We extracted the expression levels of these m6A-related genes using the “limma” package in R, and differential expression between TB patients and healthy controls was assessed via the Wilcoxon test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39622968", "locator": "sections[5] \"Identification of differentially expressed m6a regulatory genes in TB patients\" paragraph 2", "quote": "We extracted the expression levels of these m6A-related genes using the “limma” package in R, and differential expression between TB patients and healthy controls was assessed via the Wilcoxon test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The direct links required to find each data set in the database are as follows: the GEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456. The data set downloaded by this direct link is the original data set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39622968", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The direct links required to find each data set in the database are as follows: the GEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456. The data set downloaded by this direct link is the original data set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39622968", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model."}, {"asset": "source:pmid:39622968", "locator": "sections[4] \"Data collection and processing\" paragraph 2", "quote": "For data collection, we obtained the GSE83456 tuberculosis dataset from the GEO database, which includes peripheral blood samples from 45 tuberculosis (TB) patients and 61 healthy controls."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model.", "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:39622968"], "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:GSE83456"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE83456"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39622968", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-81790-4", "sha256": "sha256:988d0bcaf85e4a2f3cf4bf23a6700b61c2e51559bdfa3f805b7074d915e4c7de", "bytes": 39465, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-81790-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11612281", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE83456", "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=GSE83456", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE83456", "version": null, "note": "The direct links required to find each data set in the database are as follows: the GEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456. The data set downloaded by this direct link is the original data set.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The direct links required to find each data set in the database are as follows: the GEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456. The data set downloaded by this direct link is the original data set.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2203540", "GSM2203541", "GSM2203542", "GSM2203543", "GSM2203544", "GSM2203545", "GSM2203546", "GSM2203547", "GSM2203548", "GSM2203549", "GSM2203550", "GSM2203551", "GSM2203552", "GSM2203553", "GSM2203554", "GSM2203555", "GSM2203556", "GSM2203557", "GSM2203558", "GSM2203559", "GSM2203560", "GSM2203561", "GSM2203562", "GSM2203563", "GSM2203564", "GSM2203565", "GSM2203566", "GSM2203567", "GSM2203568", "GSM2203569", "GSM2203570", "GSM2203571", "GSM2203572", "GSM2203573", "GSM2203574", "GSM2203575", "GSM2203576", "GSM2203577", "GSM2203578", "GSM2203579", "GSM2203580", "GSM2203581", "GSM2203582", "GSM2203583", "GSM2203584", "GSM2203585", "GSM2203586", "GSM2203587", "GSM2203588", "GSM2203589", "GSM2203590", "GSM2203591", "GSM2203592", "GSM2203593", "GSM2203594", "GSM2203595", "GSM2203596", "GSM2203597", "GSM2203598", "GSM2203599", "GSM2203600", "GSM2203601", "GSM2203602", "GSM2203603", "GSM2203604", "GSM2203605", "GSM2203606", "GSM2203607", "GSM2203608", "GSM2203609", "GSM2203610", "GSM2203611", "GSM2203612", "GSM2203613", "GSM2203614", "GSM2203615", "GSM2203616", "GSM2203617", "GSM2203618", "GSM2203619", "GSM2203620", "GSM2203621", "GSM2203622", "GSM2203623", "GSM2203624", "GSM2203625", "GSM2203626", "GSM2203627", "GSM2203628", "GSM2203629", "GSM2203630", "GSM2203631", "GSM2203632", "GSM2203633", "GSM2203634", "GSM2203635", "GSM2203636", "GSM2203637", "GSM2203638", "GSM2203639", "GSM2203640", "GSM2203641", "GSM2203642", "GSM2203643", "GSM2203644", "GSM2203645", "GSM2203646", "GSM2203647", "GSM2203648", "GSM2203649", "GSM2203650", "GSM2203651", "GSM2203652", "GSM2203653", "GSM2203654", "GSM2203655", "GSM2203656", "GSM2203657", "GSM2203658", "GSM2203659", "GSM2203660", "GSM2203661", "GSM2203662", "GSM2203663", "GSM2203664", "GSM2203665", "GSM2203666", "GSM2203667", "GSM2203668", "GSM2203669", "GSM2203670", "GSM2203671", "GSM2203672", "GSM2203673", "GSM2203674", "GSM2203675", "GSM2203676", "GSM2203677", "GSM2203678", "GSM2203679", "GSM2203680", "GSM2203681", "GSM2203682", "GSM2203683", "GSM2203684", "GSM2203685", "GSM2203686", "GSM2203687", "GSM2203688", "GSM2203689", "GSM2203690", "GSM2203691", "GSM2203692", "GSM2203693", "GSM2203694", "GSM2203695", "GSM2203696", "GSM2203697", "GSM2203698", "GSM2203699", "GSM2203700", "GSM2203701", "GSM2203702", "GSM2203703", "GSM2203704", "GSM2203705", "GSM2203706", "GSM2203707", "GSM2203708", "GSM2203709", "GSM2203710", "GSM2203711", "GSM2203712", "GSM2203713", "GSM2203714", "GSM2203715", "GSM2203716", "GSM2203717", "GSM2203718", "GSM2203719", "GSM2203720", "GSM2203721", "GSM2203722", "GSM2203723", "GSM2203724", "GSM2203725", "GSM2203726", "GSM2203727", "GSM2203728", "GSM2203729", "GSM2203730", "GSM2203731", "GSM2203732", "GSM2203733", "GSM2203734", "GSM2203735", "GSM2203736", "GSM2203737", "GSM2203738", "GSM2203739", "GSM2203740", "GSM2203741"], "sample_count": 202, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "The transcriptional signature of active tuberculosis reflects symptom status in extra-pulmonary and pulmonary tuberculosis", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE83456 not downloaded", "asset:data:geo:GSE83456 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We extracted the expression levels of these m6A-related genes using the “limma” package in R, and differential expression between TB patients and healthy controls was assessed via the Wilcoxon test."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "principal component analysis"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-4", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Random Forest"}}, {"id": "method-5", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Support Vector Machine"}}, {"id": "method-6", "family": "machine_learning", "specific": "gradient_boosting", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Gradient Boosting"}}, {"id": "method-7", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.group_comparison_wilcoxon"]}, "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": false, "required_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: wilcoxon."}, "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": ["GSE83456"], "resolved_accessions": ["GSE83456"], "statements": ["The direct links required to find each data set in the database are as follows: the GEO gene expression and clinical pathology data set: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456. The data set downloaded by this direct link is the original data set."], "basis": "The paper names GSE83456 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:39622968", "data:geo:GSE83456"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "group_comparison_wilcoxon", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE83456"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:40:23Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10560, "completion_tokens": 1293, "cached_tokens": 128}}, "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:39622968", "type": "paper", "label": "pmid:39622968"}, {"id": "source:pmid:39622968", "type": "manuscript", "label": "PMID 39622968 full text"}, {"id": "data:geo:GSE83456", "type": "dataset", "label": "GEO GSE83456"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39622968", "type": "paper", "label": "pmid:39622968"}, {"id": "source:pmid:39622968", "type": "manuscript", "label": "PMID 39622968 full text"}, {"id": "data:geo:GSE83456", "type": "dataset", "label": "GEO GSE83456"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39622968", "type": "extraction", "used": ["source:pmid:39622968"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE83456", "type": "accession_resolution", "used": ["data:geo:GSE83456"], "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:39622968", "parent_hash": "sha256:129675815dd83c0fef8201d7794d22784b7226a50effe6c968855faee0616482", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE83456 licence not read", "asset:data:geo:GSE83456 not downloaded", "execution.not_run", "method.group_comparison_wilcoxon", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:group_comparison_wilcoxon"], "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": ["dentified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key", "tified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key", "ated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expressio", "of primers used quantitative real-time PCR (RT-qPCR).GeneForward primer (5’ to 3’)Reverse primer"], "order_basis": "paper_text_order:1626", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We extracted the expression levels of these m6A-related genes using the “limma” package in R, and differential expression between TB patients and healthy controls was assessed via the Wilcoxon test."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}