Machine learning identifies MiRNA biomarkers and immune mechanisms in active tuberculosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41102315", "kind": "science-record", "revision": 4, "content_hash": "sha256:85153c5824e613d67e944cee1d4da3c7ba72ea021251155db4bc558fc6525c22", "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 identifies MiRNA biomarkers and immune mechanisms in active tuberculosis.", "doi": "10.1038/s41598-025-20112-8", "pmid": "41102315", "authors": ["Zihan Cai", "Chunxiao Huang", "Yuyang Zhou", "Lixian Wu", "Shoupeng Ding"], "journal": "Scientific reports", "year": 2025, "abstract": "Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global public health threat. The rising prevalence of HIV/TB co-infection and multidrug-resistant tuberculosis (MDR-TB) has further intensified this challenge. This study aims to explore the role of microRNAs (miRNAs) in the immune response to Mtb infection and to identify potential miRNA biomarkers for active TB diagnosis using machine learning techniques. miRNA expression profiles were retrieved from the Gene Expression Omnibus (GEO) database (accession number: GSE70425). Differential expression analysis between active TB and latent TB infection (LTBI) patients was conducted using the \"limma\" package, with a screening threshold of |logFC| > 0.25 and p-value < 0.05. Key differentially expressed miRNAs (DE-miRNAs) were further refined using machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and Boruta. TargetScan was employed to predict miRNA target genes, and a regulatory network was visualized using Cytoscape. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to elucidate the functional roles of target mRNAs. Nine machine learning models were developed based on the selected miRNAs, and their predictive performance was assessed using metrics including AUROC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In vitro experiments were performed using THP-1 macrophages to establish an Mtb infection model. Cells were transfected with miR-3607-3p mimics and negative controls, followed by flow cytometry for apoptosis detection, Western blot for protein expression analysis, and Quantitative Real-Time PCR (qRT-PCR) for validation of apoptosis-related gene expression. A total of 72 differentially expressed miRNAs were identified. Key miRNAs, including hsa-miR-3607-3p, hsa-miR-148b, and hsa-miR-519e, were identified through multiple machine learning methods, with hsa-miR-3607-3p emerging as the primary candidate miRNA. The nine machine learning models exhibited robust predictive performance in both the training and test sets. PPI network and functional enrichment analyses indicated that the target genes of hsa-miR-3607-3p are primarily associated with cell growth and apoptosis-related pathways. In vitro experiments further suggested that hsa-miR-3607-3p may modulate the apoptotic response of THP-1 cells to Mtb infection via a caspase-dependent mechanism. miR-3607-3p was upregulated in active TB patients and may modulate the apoptosis of THP-1 cells during Mtb infection via a caspase-dependent pathway. The mechanism of this miRNA offers preliminary insights into the immune regulation of tuberculosis. miR-3607-3p may serve as a potential biomarker for the early diagnosis and intervention of active TB; however, its clinical applicability necessitates further validation through larger sample sizes and multicenter studies.", "claims": [{"id": "claim-method", "statement": "To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[4] \"Differential expression analysis of MiRNAs\" paragraph 2", "quote": "To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma differential expression analysis on the GSE70425 dataset, the study identified 72 differentially expressed miRNAs and identified hsa-miR-3607-3p as the primary candidate biomarker for distinguishing active TB from latent TB infection.", "kind": "quantitative", "target": {"value": 0.98, "unit": "auc", "tolerance": null, "source": "ROC curve analysis revealed that hsa-miR-3607-3p alone achieved an AUC of 0.980 in distinguishing between ATB and LTBI (Fig. 4D), indicating its exceptional diagnostic performance."}, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Differential expression analysis between active TB and latent TB infection (LTBI) patients was conducted using the “limma” package, with a screening threshold of |logFC| > 0.25 and p-value < 0.05."}, {"asset": "source:pmid:41102315", "locator": "sections[3] \"Collection and processing of expression data\" paragraph 2", "quote": "miRNA expression profiles associated with Mtb infection were retrieved from the GEO database (accession number: GSE70425)."}, {"asset": "source:pmid:41102315", "locator": "sections[19] \"Differential expression analysis\" paragraph 2", "quote": "Using stringent criteria of p-value < 0.05 and False Discovery Rate (FDR) < 0.05, 72 miRNAs were found to be differentially expressed."}, {"asset": "source:pmid:41102315", "locator": "sections[21] \"Selection and diagnostic performance of target MiRNAs\" paragraph 2", "quote": "ROC curve analysis revealed that hsa-miR-3607-3p alone achieved an AUC of 0.980 in distinguishing between ATB and LTBI (Fig. 4D), indicating its exceptional diagnostic performance."}, {"asset": "source:pmid:41102315", "locator": "sections[21] \"Selection and diagnostic performance of target MiRNAs\" paragraph 2", "quote": "Consequently, hsa-miR-3607-3p was identified as the key candidate miRNA in this study."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma differential expression analysis on the GSE70425 dataset, the study identified 72 differentially expressed miRNAs and identified hsa-miR-3607-3p as the primary candidate biomarker for distinguishing active TB from latent TB infection.", "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:41102315"], "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:GSE70425"], "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:GSE70425"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41102315", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-20112-8", "sha256": "sha256:be7e3a6513fdd641c2fe207430776b345fdacbe8debd9736ad6fbf92a76670f6", "bytes": 49575, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-20112-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12533189", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE70425", "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=GSE70425", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70425", "version": null, "note": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1754602", "GSM1754603", "GSM1754604", "GSM1754605", "GSM1754606", "GSM1754607", "GSM1754608", "GSM1754609", "GSM1754610", "GSM1754611", "GSM1754612", "GSM1754613", "GSM1754615", "GSM1754616", "GSM1754617", "GSM1754618", "GSM1754619", "GSM1754620", "GSM1754621", "GSM1754622", "GSM1754623", "GSM1754625", "GSM1754626", "GSM1754627", "GSM1754628", "GSM1754629", "GSM1754630", "GSM1754631", "GSM1754632", "GSM1754633", "GSM1754635", "GSM1754636"], "sample_count": 32, "group_sizes": null, "group_sizes_basis": null, "platform": "15159", "taxon": "Homo sapiens", "title": "Cell-specific microRNA expression patterns in TB patients and household contacts", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE70425 not downloaded", "asset:data:geo:GSE70425 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R."}}, {"id": "method-2", "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": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-4", "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-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "PPI"}}, {"id": "method-6", "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"}}, {"id": "method-7", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.7, "band": "medium", "quote": "AUROC"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "bootstrap_mean_difference", "expected_value": 0.98, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-p-value", "claim_id": "claim-target-result", "operation": "bootstrap_mean_difference", "expected_value": 0.05, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "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": ["GSE70425"], "resolved_accessions": ["GSE70425"], "statements": ["The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards."], "basis": "The paper names GSE70425 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:41102315", "data:geo:GSE70425"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE70425"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:03:18Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13338, "completion_tokens": 1712, "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:41102315", "type": "paper", "label": "pmid:41102315"}, {"id": "source:pmid:41102315", "type": "manuscript", "label": "PMID 41102315 full text"}, {"id": "data:geo:GSE70425", "type": "dataset", "label": "GEO GSE70425"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41102315", "type": "paper", "label": "pmid:41102315"}, {"id": "source:pmid:41102315", "type": "manuscript", "label": "PMID 41102315 full text"}, {"id": "data:geo:GSE70425", "type": "dataset", "label": "GEO GSE70425"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41102315", "type": "extraction", "used": ["source:pmid:41102315"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE70425", "type": "accession_resolution", "used": ["data:geo:GSE70425"], "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:41102315", "parent_hash": "sha256:70ae5ecf10f4d75175f33701bb2cd9d57ca116695e849d09e12f47ee59851027", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE70425 licence not read", "asset:data:geo:GSE70425 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": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["establish an Mtb infection model. Cells were transfected with miR-3607-3p mimics and negative control"], "order_basis": "paper_text_order:1714", "id": "assay:1"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["3p mimics and negative controls, followed by flow cytometry for apoptosis detection, Western blot for pr"], "order_basis": "paper_text_order:1785", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ive controls, followed by flow cytometry for apoptosis detection, Western blot for protein expressi", "ve Real-Time PCR (qRT-PCR) for validation of apoptosis-related gene expression. A total of 72 diffe", "re primarily associated with cell growth and apoptosis-related pathways. In vitro experiments furth", "d in active TB patients and may modulate the apoptosis of THP-1 cells during Mtb infection via a ca"], "order_basis": "paper_text_order:1804", "id": "assay:3"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["d by flow cytometry for apoptosis detection, Western blot for protein expression analysis, and Quantit"], "order_basis": "paper_text_order:1825", "id": "assay:4"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["rotein expression analysis, and Quantitative Real-Time PCR (qRT-PCR) for validation of apoptosis-relate"], "order_basis": "paper_text_order:1888", "id": "assay:5"}, {"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": ["To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}