A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39418100", "kind": "science-record", "revision": 4, "content_hash": "sha256:eae1f1ae758b370d07cfc13d74c2cded84218f3eea5333411255c8fc271bbe52", "created_at": "2026-11-01T20:29:39Z", "updated_at": "2026-11-01T22:55:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation.", "doi": "10.18632/aging.205873", "pmid": "39418100", "authors": ["Jianhua Yang", "Wenjun Li", "Xuemei Lin", "Wei Liang"], "journal": "Aging", "year": 2024, "abstract": "BACKGROUND: Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA).\nMETHODS: Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes.\nRESULTS: Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05).\nCONCLUSIONS: A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.", "claims": [{"id": "claim-method", "statement": "Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39418100", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39418100", "locator": "sections[16] \"Data availability\" paragraph 2", "quote": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39418100", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7).", "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:39418100"], "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:GSE114007"], "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:machine_learning_random_forest", "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:GSE114007"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39418100", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205873", "sha256": "sha256:827d8569d71e0a009989e8b3e9c083b38ced7629f37c41cca4744f6737734aa9", "bytes": 42238, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.205873", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11552637", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE114007", "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=GSE114007", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE114007", "version": null, "note": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3130531", "GSM3130532", "GSM3130533", "GSM3130534", "GSM3130535", "GSM3130536", "GSM3130537", "GSM3130538", "GSM3130539", "GSM3130540", "GSM3130541", "GSM3130542", "GSM3130543", "GSM3130544", "GSM3130545", "GSM3130546", "GSM3130547", "GSM3130548", "GSM3130549", "GSM3130550", "GSM3130551", "GSM3130552", "GSM3130553", "GSM3130554", "GSM3130555", "GSM3130556", "GSM3130557", "GSM3130558", "GSM3130559", "GSM3130560", "GSM3130561", "GSM3130562", "GSM3130563", "GSM3130564", "GSM3130565", "GSM3130566", "GSM3130567", "GSM3130568"], "sample_count": 38, "group_sizes": null, "group_sizes_basis": null, "platform": "11154;18573", "taxon": "Homo sapiens", "title": "Identification of transcription factors responsible for dysregulated networks in human osteoarthritis cartilage by global gene expression analysis", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE117999", "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=GSE117999", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE117999", "version": null, "note": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3316962", "GSM3316963", "GSM3316964", "GSM3316965", "GSM3316966", "GSM3316967", "GSM3316968", "GSM3316969", "GSM3316970", "GSM3316971", "GSM3316972", "GSM3316973", "GSM3316974", "GSM3316975", "GSM3316976", "GSM3316977", "GSM3316978", "GSM3316979", "GSM3316980", "GSM3316981", "GSM3316982", "GSM3316983", "GSM3316984", "GSM3316985"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "20844", "taxon": "Homo sapiens", "title": "Transcriptome comparison of cartilage from patients with and without osteoarthritis", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE51588", "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=GSE51588", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE51588", "version": null, "note": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1248759", "GSM1248760", "GSM1248761", "GSM1248762", "GSM1248763", "GSM1248764", "GSM1248765", "GSM1248766", "GSM1248767", "GSM1248768", "GSM1248769", "GSM1248770", "GSM1248771", "GSM1248772", "GSM1248773", "GSM1248774", "GSM1248775", "GSM1248776", "GSM1248777", "GSM1248778", "GSM1248779", "GSM1248780", "GSM1248781", "GSM1248782", "GSM1248783", "GSM1248784", "GSM1248785", "GSM1248786", "GSM1248787", "GSM1248788", "GSM1248789", "GSM1248790", "GSM1248791", "GSM1248792", "GSM1248793", "GSM1248794", "GSM1248795", "GSM1248796", "GSM1248797", "GSM1248798", "GSM1248799", "GSM1248800", "GSM1248801", "GSM1248802", "GSM1248803", "GSM1248804", "GSM1248805", "GSM1248806", "GSM1248807", "GSM1248808"], "sample_count": 50, "group_sizes": null, "group_sizes_basis": null, "platform": "13497", "taxon": "Homo sapiens", "title": "Genome-wide Expression Profiles of Subchondral Bone in Osteoarthritis", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE82107", "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=GSE82107", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE82107", "version": null, "note": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2183532", "GSM2183533", "GSM2183534", "GSM2183535", "GSM2183536", "GSM2183537", "GSM2183538", "GSM2183539", "GSM2183540", "GSM2183541", "GSM2183542", "GSM2183543", "GSM2183544", "GSM2183545", "GSM2183546", "GSM2183547", "GSM2183548"], "sample_count": 17, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Synovial biopsies of osteoarthritis patients", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE89408", "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=GSE89408", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE89408", "version": null, "note": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2370970", "GSM2370971", "GSM2370972", "GSM2370973", "GSM2370974", "GSM2370975", "GSM2370976", "GSM2370977", "GSM2370978", "GSM2370979", "GSM2370980", "GSM2370981", "GSM2370982", "GSM2370983", "GSM2370984", "GSM2370985", "GSM2370986", "GSM2370987", "GSM2370988", "GSM2370989", "GSM2370990", "GSM2370991", "GSM2370992", "GSM2370993", "GSM2370994", "GSM2370995", "GSM2370996", "GSM2370997", "GSM2370998", "GSM2370999", "GSM2371000", "GSM2371001", "GSM2371002", "GSM2371003", "GSM2371004", "GSM2371005", "GSM2371006", "GSM2371007", "GSM2371008", "GSM2371009", "GSM2371010", "GSM2371011", "GSM2371012", "GSM2371013", "GSM2371014", "GSM2371015", "GSM2371016", "GSM2371017", "GSM2371018", "GSM2371019", "GSM2371020", "GSM2371021", "GSM2371022", "GSM2371023", "GSM2371024", "GSM2371025", "GSM2371026", "GSM2371027", "GSM2371028", "GSM2371029", "GSM2371030", "GSM2371031", "GSM2371032", "GSM2371033", "GSM2371034", "GSM2371035", "GSM2371036", "GSM2371037", "GSM2371038", "GSM2371039", "GSM2371040", "GSM2371041", "GSM2371042", "GSM2371043", "GSM2371044", "GSM2371045", "GSM2371046", "GSM2371047", "GSM2371048", "GSM2371049", "GSM2371050", "GSM2371051", "GSM2371052", "GSM2371053", "GSM2371054", "GSM2371055", "GSM2371056", "GSM2371057", "GSM2371058", "GSM2371059", "GSM2371060", "GSM2371061", "GSM2371062", "GSM2371063", "GSM2371064", "GSM2371065", "GSM2371066", "GSM2371067", "GSM2371068", "GSM2371069", "GSM2371070", "GSM2371071", "GSM2371072", "GSM2371073", "GSM2371074", "GSM2371075", "GSM2371076", "GSM2371077", "GSM2371078", "GSM2371079", "GSM2371080", "GSM2371081", "GSM2371082", "GSM2371083", "GSM2371084", "GSM2371085", "GSM2371086", "GSM2371087", "GSM2371088", "GSM2371089", "GSM2371090", "GSM2371091", "GSM2371092", "GSM2371093", "GSM2371094", "GSM2371095", "GSM2371096", "GSM2371097", "GSM2371098", "GSM2371099", "GSM2371100", "GSM2371101", "GSM2371102", "GSM2371103", "GSM2371104", "GSM2371105", "GSM2371106", "GSM2371107", "GSM2371108", "GSM2371109", "GSM2371110", "GSM2371111", "GSM2371112", "GSM2371113", "GSM2371114", "GSM2371115", "GSM2371116", "GSM2371117", "GSM2371118", "GSM2371119", "GSM2371120", "GSM2371121", "GSM2371122", "GSM2371123", "GSM2371124", "GSM2371125", "GSM2371126", "GSM2371127", "GSM2371128", "GSM2371129", "GSM2371130", "GSM2371131", "GSM2371132", "GSM2371133", "GSM2371134", "GSM2371135", "GSM2371136", "GSM2371137", "GSM2371138", "GSM2371139", "GSM2371140", "GSM2371141", "GSM2371142", "GSM2371143", "GSM2371144", "GSM2371145", "GSM2371146", "GSM2371147", "GSM2371148", "GSM2371149", "GSM2371150", "GSM2371151", "GSM2371152", "GSM2371153", "GSM2371154", "GSM2371155", "GSM2371156", "GSM2371157", "GSM2371158", "GSM2371159", "GSM2371160", "GSM2371161", "GSM2371162", "GSM2371163", "GSM2371164", "GSM2371165", "GSM2371166", "GSM2371167", "GSM2371168", "GSM2371169", "GSM2371170", "GSM2371171", "GSM2371172", "GSM2371173", "GSM2371174", "GSM2371175", "GSM2371176", "GSM2371177", "GSM2371178", "GSM2371179", "GSM2371180", "GSM2371181", "GSM2371182", "GSM2371183", "GSM2371184", "GSM2371185", "GSM2371186", "GSM2371187"], "sample_count": 218, "group_sizes": null, "group_sizes_basis": null, "platform": "11154", "taxon": "Homo sapiens", "title": "RNAseq study of synovial biopsies", "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:GSE114007 not downloaded", "asset:data:geo:GSE114007 licence not read", "asset:data:geo:GSE117999 not downloaded", "asset:data:geo:GSE117999 licence not read", "asset:data:geo:GSE51588 not downloaded", "asset:data:geo:GSE51588 licence not read", "asset:data:geo:GSE82107 not downloaded", "asset:data:geo:GSE82107 licence not read", "asset:data:geo:GSE89408 not downloaded", "asset:data:geo:GSE89408 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models."}}, {"id": "method-2", "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": "SVM"}}, {"id": "method-3", "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": "XGBoost"}}, {"id": "method-4", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"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": "protein-protein interaction"}}], "targets": [], "gaps": ["method.machine_learning_random_forest"]}, "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": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "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": ["GSE51588", "GSE114007", "GSE117999", "GSE82107", "GSE89408"], "resolved_accessions": ["GSE51588", "GSE114007", "GSE117999", "GSE82107", "GSE89408"], "statements": ["The data that support the findings of this study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The R codes used in this study can be obtained from the corresponding author upon reasonable request."], "basis": "The paper names GSE51588, GSE114007, GSE117999, GSE82107, GSE89408 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": [], "assets": ["source:pmid:39418100", "data:geo:GSE114007"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE114007"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:35:28Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11711, "completion_tokens": 2109, "cached_tokens": 256}}, "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:39418100", "type": "paper", "label": "pmid:39418100"}, {"id": "source:pmid:39418100", "type": "manuscript", "label": "PMID 39418100 full text"}, {"id": "data:geo:GSE114007", "type": "dataset", "label": "GEO GSE114007"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39418100", "type": "paper", "label": "pmid:39418100"}, {"id": "source:pmid:39418100", "type": "manuscript", "label": "PMID 39418100 full text"}, {"id": "data:geo:GSE114007", "type": "dataset", "label": "GEO GSE114007"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39418100", "type": "extraction", "used": ["source:pmid:39418100"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE114007", "type": "accession_resolution", "used": ["data:geo:GSE114007"], "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:39418100", "parent_hash": "sha256:4bce260bc8f61760e20e4c0f8cf177c5c664bfc7e03c2d58ffff925abbce16c4", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE114007 licence not read", "asset:data:geo:GSE114007 not downloaded", "asset:data:geo:GSE117999 licence not read", "asset:data:geo:GSE117999 not downloaded", "asset:data:geo:GSE51588 licence not read", "asset:data:geo:GSE51588 not downloaded", "asset:data:geo:GSE82107 licence not read", "asset:data:geo:GSE82107 not downloaded", "asset:data:geo:GSE89408 licence not read", "asset:data:geo:GSE89408 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["the local hospital for re-verification. The transfection assays were conducted to explore the regulat", "lated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promot", "previously described [22, 23]. The transient transfection assays in the CHON-001 cells were conducted", "T-qPCR) experiments were used to measure the knockdown efficacy of the siRNAs. Supplementary Materi"], "order_basis": "paper_text_order:1024", "id": "assay:1"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["re the regulatory ability of the gene to the apoptosis and vitality of chondrocytes. RESULTS: Throu", "FB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta"], "order_basis": "paper_text_order:1111", "id": "assay:2"}, {"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": ["mentary Table 4. Real-time quantitative PCR (RT-qPCR) experiments were used to measure the knockd", "tary Table 4. Real-time quantitative PCR (RT-qPCR) experiments were used to measure the knockd", "ences can be found in Supplementary Table 4. Real-time quantitative PCR (RT-qPCR) experiments were used to measure t"], "order_basis": "paper_text_order:3441", "id": "assay:3"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}