Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36891318", "kind": "science-record", "revision": 3, "content_hash": "sha256:eca28590e708d970dccd5157b49e8a4342afd1e1fbef731272f4a95d0c893ac9", "created_at": "2026-11-01T19:01:31Z", "updated_at": "2026-11-01T22:09:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis.", "doi": "10.3389/fimmu.2023.1103509", "pmid": "36891318", "authors": ["Yu Zhou", "Xin Li", "Liqi Ng", "Qing Zhao", "Wentao Guo", "Jinhua Hu", "Jinghong Zhong", "Wenlong Su", "Chaozong Liu", "Songchuan Su"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Objective: An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.\nMethods: Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.\nResults: The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.\nConclusion: This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.", "claims": [{"id": "claim-method", "statement": "The ROC curves of the four models were then plotted based on the 5-fold cross-validation to comprehensively assess their discrimination performance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36891318", "locator": "sections[21] \"Construction and evaluation of machine learning models\" paragraph 2", "quote": "The ROC curves of the four models were then plotted based on the 5-fold cross-validation to comprehensively assess their discrimination performance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36891318", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest model was the best-performing machine-learning model, with an AUC of 0.843, and identified SLC35A1, PRPF39, MAP4K3, TMX1 and FAM96A as predictive genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36891318", "locator": "sections[21] \"Construction and evaluation of machine learning models\" paragraph 2", "quote": "The AUCs of the four models were, in order, RF: AUC = 0.843; SVM, AUC = 0.746; XGB, AUC = 0.710; and GLM, AUC = 0.773 (Figure 9D)."}, {"asset": "source:pmid:36891318", "locator": "sections[21] \"Construction and evaluation of machine learning models\" paragraph 2", "quote": "the RF model ultimately obtained five significant genes (SLC35A1, PRPF39, MAP4K3, TMX1 and FAM96A), which were used as predictive genes for subsequent analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The random forest model was the best-performing machine-learning model, with an AUC of 0.843, and identified SLC35A1, PRPF39, MAP4K3, TMX1 and FAM96A as predictive genes.", "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:36891318"], "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:GSE93272"], "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": {"cross_validation_folds": "andom forest model with 5-fold cross-validation"}, "inputs": ["data:geo:GSE93272"], "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:36891318", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1103509", "sha256": "sha256:80acf72b4e79d303762a1437ee2018c1094e67498e21659a9863e77724288990", "bytes": 42443, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1103509", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9986609", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE93272", "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=GSE93272", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE93272", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2449608", "GSM2449609", "GSM2449610", "GSM2449611", "GSM2449612", "GSM2449613", "GSM2449614", "GSM2449615", "GSM2449616", "GSM2449617", "GSM2449618", "GSM2449619", "GSM2449620", "GSM2449621", "GSM2449622", "GSM2449623", "GSM2449624", "GSM2449625", "GSM2449626", "GSM2449627", "GSM2449628", "GSM2449629", "GSM2449630", "GSM2449631", "GSM2449632", "GSM2449633", "GSM2449634", "GSM2449635", "GSM2449636", "GSM2449637", "GSM2449638", "GSM2449639", "GSM2449640", "GSM2449641", "GSM2449642", "GSM2449643", "GSM2449644", "GSM2449645", "GSM2449646", "GSM2449647", "GSM2449648", "GSM2449649", "GSM2449650", "GSM2449651", "GSM2449652", "GSM2449653", "GSM2449654", "GSM2449655", "GSM2449656", "GSM2449657", "GSM2449658", "GSM2449659", "GSM2449660", "GSM2449661", "GSM2449662", "GSM2449663", "GSM2449664", "GSM2449665", "GSM2449666", "GSM2449667", "GSM2449668", "GSM2449669", "GSM2449670", "GSM2449671", "GSM2449672", "GSM2449673", "GSM2449674", "GSM2449675", "GSM2449676", "GSM2449677", "GSM2449678", "GSM2449679", "GSM2449680", "GSM2449682", "GSM2449683", "GSM2449684", "GSM2449685", "GSM2449686", "GSM2449687", "GSM2449688", "GSM2449689", "GSM2449690", "GSM2449691", "GSM2449692", "GSM2449693", "GSM2449694", "GSM2449695", "GSM2449696", "GSM2449697", "GSM2449698", "GSM2449699", "GSM2449700", "GSM2449701", "GSM2449702", "GSM2449703", "GSM2449704", "GSM2449705", "GSM2449706", "GSM2449707", "GSM2449708", "GSM2449709", "GSM2449710", "GSM2449711", "GSM2449712", "GSM2449713", "GSM2449714", "GSM2449715", "GSM2449716", "GSM2449717", "GSM2449718", "GSM2449719", "GSM2449720", "GSM2449721", "GSM2449722", "GSM2449723", "GSM2449724", "GSM2449725", "GSM2449726", "GSM2449727", "GSM2449728", "GSM2449729", "GSM2449730", "GSM2449731", "GSM2449732", "GSM2449733", "GSM2449734", "GSM2449735", "GSM2449736", "GSM2449737", "GSM2449738", "GSM2449739", "GSM2449740", "GSM2449741", "GSM2449742", "GSM2449743", "GSM2449744", "GSM2449745", "GSM2449746", "GSM2449747", "GSM2449748", "GSM2449749", "GSM2449750", "GSM2449751", "GSM2449752", "GSM2449753", "GSM2449754", "GSM2449755", "GSM2449756", "GSM2449757", "GSM2449758", "GSM2449759", "GSM2449760", "GSM2449761", "GSM2449762", "GSM2449763", "GSM2449764", "GSM2449765", "GSM2449766", "GSM2449767", "GSM2449768", "GSM2449769", "GSM2449770", "GSM2449771", "GSM2449772", "GSM2449773", "GSM2449774", "GSM2449775", "GSM2449776", "GSM2449777", "GSM2449778", "GSM2449779", "GSM2449780", "GSM2449781", "GSM2449782", "GSM2449783", "GSM2449784", "GSM2449785", "GSM2449786", "GSM2449789", "GSM2449790", "GSM2449791", "GSM2449792", "GSM2449793", "GSM2449794", "GSM2449795", "GSM2449796", "GSM2449797", "GSM2449798", "GSM2449799", "GSM2449800", "GSM2449801", "GSM2449802", "GSM2449803", "GSM2449804", "GSM2449805", "GSM2449806", "GSM2449807", "GSM2449808", "GSM2449809", "GSM2449810", "GSM2449811", "GSM2449812", "GSM2449813", "GSM2449814", "GSM2449815", "GSM2449816", "GSM2449817", "GSM2449818", "GSM2449819", "GSM2449820", "GSM2449821", "GSM2449822", "GSM2449823", "GSM2449824", "GSM2449825", "GSM2449826", "GSM2449827", "GSM2449828", "GSM2449829", "GSM2449830", "GSM2449831", "GSM2449832", "GSM2449833", "GSM2449834", "GSM2449835", "GSM2449836", "GSM2449837", "GSM2449839", "GSM2449840", "GSM2449841", "GSM2449842", "GSM2449843", "GSM2449844", "GSM2449845", "GSM2449846", "GSM2449847", "GSM2449848", "GSM2449849", "GSM2449850", "GSM2449851", "GSM2449852", "GSM2449853", "GSM2449854", "GSM2449855", "GSM2449856", "GSM2449857", "GSM2449858", "GSM2449859", "GSM2449860", "GSM2449861", "GSM2449863", "GSM2449864", "GSM2449865", "GSM2449866", "GSM2449867", "GSM2449868", "GSM2449869", "GSM2449870", "GSM2449871", "GSM2449872", "GSM2449873", "GSM2449874", "GSM2449875", "GSM2449876", "GSM2449877", "GSM2449878", "GSM2449879", "GSM2449880", "GSM2449881", "GSM2449882", "GSM2449883", "GSM2449884", "GSM2449885", "GSM2449886", "GSM2449887"], "sample_count": 275, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Whole blood gene expression of rheumatoid arthritis", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE93272 not downloaded", "asset:data:geo:GSE93272 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": {"cross_validation_folds": "andom forest model with 5-fold cross-validation"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The ROC curves of the four models were then plotted based on the 5-fold cross-validation to comprehensively assess their discrimination performance."}}, {"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": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-3", "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": 0.8, "band": "high", "quote": "support vector machine"}}, {"id": "method-4", "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": 0.8, "band": "high", "quote": "Gradient Boosting"}}, {"id": "method-5", "family": "regression_modelling", "specific": "ols", "label": "Regression modelling", "dsl_operations": ["ols_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "linear regression"}}, {"id": "method-6", "family": "clustering", "specific": "kmeans", "label": "Clustering", "dsl_operations": ["kmeans_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "k-means"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor", "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": ["GSE93272"], "resolved_accessions": ["GSE93272"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."], "basis": "The paper names GSE93272 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:36891318", "data:geo:GSE93272"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE93272"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:33:00Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11566, "completion_tokens": 1406, "cached_tokens": 264}}, "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:36891318", "type": "paper", "label": "pmid:36891318"}, {"id": "source:pmid:36891318", "type": "manuscript", "label": "PMID 36891318 full text"}, {"id": "data:geo:GSE93272", "type": "dataset", "label": "GEO GSE93272"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36891318", "type": "paper", "label": "pmid:36891318"}, {"id": "source:pmid:36891318", "type": "manuscript", "label": "PMID 36891318 full text"}, {"id": "data:geo:GSE93272", "type": "dataset", "label": "GEO GSE93272"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36891318", "type": "extraction", "used": ["source:pmid:36891318"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE93272", "type": "accession_resolution", "used": ["data:geo:GSE93272"], "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:36891318", "parent_hash": "sha256:09d98050dad0f18cc3fd3b7a05a17b9ea80817044bb18454ef530781cef21ba9", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE93272 licence not read", "asset:data:geo:GSE93272 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "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": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["cted genes, and validated by constructing RA rat models. Results: The location of the 13 CRGs", "days later. During the moulding period, the rats were subjected to wind, cold and humidity stimula", "male SD (6 weeks old, 140g each, SPF class) rats (n = 5 in each group) were obtained from Lia", "w environment prior to the experiments. Each rat was randomly assigned to one of two groups w"], "order_basis": "paper_text_order:729", "id": "assay:1"}, {"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": ["The ROC curves of the four models were then plotted based on the 5-fold cross-validation to comprehensively assess their discrimination performance."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}