Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with dilated cardiomyopathy (DCM) by bioinformatics analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40520229", "kind": "science-record", "revision": 4, "content_hash": "sha256:c46ec059c5fd9e64001b295f125c0b24d5b54c9d11a25c66eabc29049a641bfb", "created_at": "2026-11-01T20:30:37Z", "updated_at": "2026-11-01T22:56:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with dilated cardiomyopathy (DCM) by bioinformatics analysis and machine learning.", "doi": "10.3389/fgene.2025.1562891", "pmid": "40520229", "authors": ["Yuhang Liu", "Yong Wang", "Wenyang Nie", "Zhen Wang"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Background: Chronic kidney disease (CKD) is a globally prevalent and highly lethal condition, often accompanied by dilated cardiomyopathy (DCM), which increases the risk of cardiac complications. Early detection of DCM in CKD patients remains challenging, despite established research demonstrating the relationship between CKD and cardiac abnormalities.\nMethods: We retrieved expression matrices for DCM (GSE57338, GSE29819) and CKD (GSE104954) from GEO and a DCM scRNA-seq dataset (GSE145154). These were analyzed for differential gene expression and WGCNA. KEGG and GO analyses were performed on shared differentially expressed genes in DCM and CKD. Potential drugs for DCM were identified using CMAP. Machine learning methods LASSO, SVM-RFE, and RF were used to find biomarkers and develop a diagnostic nomogram for CKD-associated DCM, validated with external datasets. Single-gene GSEA was conducted to understand model gene mechanisms in CKD-associated DCM. Immune cell infiltration was analyzed with CIBERSORT, and single-cell sequencing examined model gene distribution and expression in the heart.\nResults: Our examination of the expression matrix datasets associated with DCM and CKD revealed 115 key model genes that are shared by the two disorders as well as 47 genes that are differently expressed. These 47 differentially expressed genes were primarily linked to immune regulation and inflammation, according to enrichment analysis. CMAP analysis suggested withaferin-a, droxinostat, fluorometholone, and others as potential DCM treatments. Machine learning identified MNS1 and HERC6 as significant CKD-associated DCM biomarkers. A diagnostic nomogram using these genes was developed, showing strong discriminative power and clinical utility. MNS1 and HERC6 are implicated in metabolism, inflammation, immunity, and heart function. Immune cell infiltration analysis indicated dysregulation in DCM, with MNS1 and HERC6 correlating with immune cells. Single-cell sequencing showed MNS1 and HERC6 expression in endothelial cells and fibroblasts, respectively.\nConclusion: We identified MNS1 and HERC6 as biomarkers and developed a new diagnostic nomogram based on them for the timely diagnosis of CKD patients presenting with DCM complications. This study's findings offer novel insights into potential diagnostic methods and therapeutic strategies regarding the coexistence of CKD and DCM.", "claims": [{"id": "claim-method", "statement": "To evaluate the model genes, we first performed multivariate logistic regression analyses. Subsequently, we constructed a diagnostic nomogram using the “rms” package. The predictive performance of both individual model genes and the integrated diagnostic model was assessed through ROC analysis implemented via the “ROCR” package. To validate the clinical utility and predictive accuracy of the nomogram, we employed Decision Curve Analysis (DCA) and calibration curves, which were generated using the “calibration” function from the “rmda” package and the “rms” package, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40520229", "locator": "sections[9] \"2.7 Construction of column line diagrams and evaluation of predictive models for diagnostic markers\" paragraph 2", "quote": "To evaluate the model genes, we first performed multivariate logistic regression analyses. Subsequently, we constructed a diagnostic nomogram using the “rms” package. The predictive performance of both individual model genes and the integrated diagnostic model was assessed through ROC analysis implemented via the “ROCR” package. To validate the clinical utility and predictive accuracy of the nomogram, we employed Decision Curve Analysis (DCA) and calibration curves, which were generated using the “calibration” function from the “rmda” package and the “rms” package, respectively."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40520229", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MNS1 and HERC6 were identified as biomarkers, and a diagnostic nomogram based on them was developed for the timely diagnosis of CKD patients presenting with DCM complications.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40520229", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We identified MNS1 and HERC6 as biomarkers and developed a new diagnostic nomogram based on them for the timely diagnosis of CKD patients presenting with DCM complications."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "MNS1 and HERC6 were identified as biomarkers, and a diagnostic nomogram based on them was developed for the timely diagnosis of CKD patients presenting with DCM complications.", "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:40520229"], "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:GSE104954"], "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:GSE104954"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40520229", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1562891", "sha256": "sha256:784fd965d02fd1d20242ab2c1f171935b73998248c01f4ce8d589f8c9f08866e", "bytes": 48542, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1562891", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12162942", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104954", "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=GSE104954", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104954", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2810894", "GSM2810895", "GSM2810896", "GSM2810897", "GSM2810898", "GSM2810899", "GSM2810900", "GSM2810901", "GSM2810902", "GSM2810903", "GSM2810904", "GSM2810905", "GSM2810906", "GSM2810907", "GSM2810908", "GSM2810909", "GSM2810910", "GSM2810911", "GSM2810912", "GSM2810913", "GSM2810914", "GSM2810915", "GSM2810916", "GSM2810917", "GSM2810918", "GSM2810919", "GSM2810920", "GSM2810921", "GSM2810922", "GSM2810923", "GSM2810924", "GSM2810925", "GSM2810926", "GSM2810927", "GSM2810928", "GSM2810929", "GSM2810930", "GSM2810931", "GSM2810932", "GSM2810933", "GSM2810934", "GSM2810935", "GSM2810936", "GSM2810937", "GSM2810938", "GSM2810939", "GSM2810940", "GSM2810941", "GSM2810942", "GSM2810943", "GSM2810944", "GSM2810945", "GSM2810946", "GSM2810947", "GSM2810948", "GSM2810949", "GSM2810950", "GSM2810951", "GSM2810952", "GSM2810953", "GSM2810954", "GSM2810955", "GSM2810956", "GSM2810957", "GSM2810958", "GSM2810959", "GSM2810960", "GSM2810961", "GSM2810962", "GSM2810963", "GSM2810964", "GSM2810965", "GSM2810966", "GSM2810967", "GSM2810968", "GSM2810969", "GSM2810970", "GSM2810971", "GSM2810972", "GSM2810973", "GSM2810974", "GSM2810975", "GSM2810976", "GSM2810977", "GSM2810978", "GSM2810979", "GSM2810980", "GSM2810981", "GSM2810982", "GSM2810983", "GSM2810984", "GSM2810985", "GSM2810986", "GSM2810987", "GSM2810988", "GSM2810989", "GSM2810990", "GSM2810991", "GSM2810992", "GSM2810993", "GSM2810994", "GSM2810995", "GSM2810996", "GSM2810997", "GSM2810998", "GSM2810999", "GSM2811000", "GSM2811001", "GSM2811002", "GSM2811003", "GSM2811004", "GSM2811005", "GSM2811006", "GSM2811007", "GSM2811008", "GSM2811009", "GSM2811010", "GSM2811011", "GSM2811012", "GSM2811013", "GSM2811014", "GSM2811015", "GSM2811016", "GSM2811017", "GSM2811018", "GSM2811019", "GSM2811020", "GSM2811021", "GSM2811022", "GSM2811023", "GSM2811024", "GSM2811025", "GSM2811026", "GSM2811027", "GSM2811028", "GSM2811029", "GSM2811030", "GSM2811031", "GSM2811032", "GSM2811033", "GSM2811034", "GSM2811035", "GSM2811036", "GSM2811037", "GSM2811038", "GSM2811039", "GSM2811040", "GSM2811041", "GSM2811042", "GSM2811043", "GSM2811044", "GSM2811045", "GSM2811046", "GSM2811047", "GSM2811048", "GSM2811049", "GSM2811050", "GSM2811051", "GSM2811052", "GSM2811053", "GSM2811054", "GSM2811055", "GSM2811056", "GSM2811057", "GSM2811058", "GSM2811059", "GSM2811060", "GSM2811061", "GSM2811062", "GSM2811063", "GSM2811064", "GSM2811065", "GSM2811066", "GSM2811067", "GSM2811068", "GSM2811069", "GSM2811070", "GSM2811071", "GSM2811072", "GSM2811073", "GSM2811074", "GSM2811075", "GSM2811076", "GSM2811077", "GSM2811078", "GSM2811079", "GSM2811080", "GSM2811081", "GSM2811082", "GSM2811083", "GSM2811084", "GSM2811085", "GSM2811086", "GSM2811087", "GSM2811088"], "sample_count": 195, "group_sizes": null, "group_sizes_basis": null, "platform": "22945;24120", "taxon": "Homo sapiens", "title": "Tubulointerstitial transcriptome from ERCB subjects with chronic kidney disease and living donor biopsies.", "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:GSE145154", "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=GSE145154", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE145154", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM738990", "GSM738991", "GSM738992", "GSM738993", "GSM738994", "GSM738995", "GSM738996", "GSM738997", "GSM738998", "GSM738999", "GSM739000", "GSM739001", "GSM739002", "GSM739003", "GSM739004", "GSM739005", "GSM739006", "GSM739007", "GSM739008", "GSM739009", "GSM739010", "GSM739011", "GSM739012", "GSM739013", "GSM739014", "GSM739015", "GSM739016", "GSM739017", "GSM739018", "GSM739019", "GSM739020", "GSM739021", "GSM739022", "GSM739023", "GSM739024", "GSM739025", "GSM739026", "GSM739027"], "sample_count": 38, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Myocardial transcriptome analysis of human arrhythmogenic right ventricular cardiomyopathy (ARVC)", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE57338", "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=GSE57338", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE57338", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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"GSM1380070", "GSM1380071", "GSM1380072", "GSM1380073", "GSM1380074", "GSM1380075", "GSM1380076", "GSM1380077", "GSM1380078", "GSM1380079", "GSM1380080", "GSM1380081", "GSM1380082", "GSM1380083", "GSM1380084", "GSM1380085", "GSM1380086", "GSM1380087", "GSM1380088", "GSM1380089", "GSM1380090", "GSM1380091", "GSM1380092", "GSM1380093", "GSM1380094", "GSM1380095", "GSM1380096", "GSM1380097", "GSM1380098", "GSM1380099", "GSM1380100", "GSM1380101", "GSM1380102", "GSM1380103", "GSM1380104", "GSM1380105", "GSM1380106", "GSM1380107", "GSM1380108", "GSM1380109", "GSM1380110", "GSM1380111", "GSM1380112", "GSM1380113", "GSM1380114", "GSM1380115", "GSM1380116", "GSM1380117", "GSM1380118", "GSM1380119", "GSM1380120", "GSM1380121", "GSM1380122", "GSM1380123", "GSM1380124"], "sample_count": 313, "group_sizes": null, "group_sizes_basis": null, "platform": "11532", "taxon": "Homo sapiens", "title": "RNA-Seq Identifies Novel Myocardial Gene Expression Signatures of Heart Failure [microarray]", "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:GSE104954 not downloaded", "asset:data:geo:GSE104954 licence not read", "asset:data:geo:GSE145154 not downloaded", "asset:data:geo:GSE145154 licence not read", "asset:data:geo:GSE29819 not downloaded", "asset:data:geo:GSE29819 licence not read", "asset:data:geo:GSE57338 not downloaded", "asset:data:geo:GSE57338 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "To evaluate the model genes, we first performed multivariate logistic regression analyses. Subsequently, we constructed a diagnostic nomogram using the “rms” package. The predictive performance of both individual model genes and the integrated diagnostic model was assessed through ROC analysis implemented via the “ROCR” package. To validate the clinical utility and predictive accuracy of the nomogram, we employed Decision Curve Analysis (DCA) and calibration curves, which were generated using the “calibration” function from the “rmda” package and the “rms” package, respectively."}}, {"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": "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": 0.8, "band": "high", "quote": "ROC analysis"}}, {"id": "method-4", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.8, "band": "high", "quote": "Decision Curve Analysis"}}], "targets": [], "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": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "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": ["GSE57338", "GSE29819", "GSE104954", "GSE145154"], "resolved_accessions": ["GSE57338", "GSE29819", "GSE104954", "GSE145154"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/) The datasets include one gene expression profile for CKD (GSE104954), two gene expression profiles for DCM (GSE57338, GSE29819), and one single-cell RNA sequencing dataset for DCM (GSE145154)."], "basis": "The paper names GSE57338, GSE29819, GSE104954, GSE145154 in Gene Expression Omnibus and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:40520229", "data:geo:GSE104954"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE104954"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:54:27Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12402, "completion_tokens": 2557, "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:40520229", "type": "paper", "label": "pmid:40520229"}, {"id": "source:pmid:40520229", "type": "manuscript", "label": "PMID 40520229 full text"}, {"id": "data:geo:GSE104954", "type": "dataset", "label": "GEO GSE104954"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40520229", "type": "paper", "label": "pmid:40520229"}, {"id": "source:pmid:40520229", "type": "manuscript", "label": "PMID 40520229 full text"}, {"id": "data:geo:GSE104954", "type": "dataset", "label": "GEO GSE104954"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40520229", "type": "extraction", "used": ["source:pmid:40520229"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE104954", "type": "accession_resolution", "used": ["data:geo:GSE104954"], "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:40520229", "parent_hash": "sha256:028ee8cbb4401c66714aad550814630eb015b631e1735164bc2ce631896ca9ce", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE104954 licence not read", "asset:data:geo:GSE104954 not downloaded", "asset:data:geo:GSE145154 licence not read", "asset:data:geo:GSE145154 not downloaded", "asset:data:geo:GSE29819 licence not read", "asset:data:geo:GSE29819 not downloaded", "asset:data:geo:GSE57338 licence not read", "asset:data:geo:GSE57338 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To evaluate the model genes, we first performed multivariate logistic regression analyses. Subsequently, we constructed a diagnostic nomogram using the “rms” package. The predictive performance of both individual model genes and the integrated diagnostic model was assessed through ROC analysis implemented via the “ROCR” package. To validate the clinical utility and predictive accuracy of the nomogram, we employed Decision Curve Analysis (DCA) and calibration curves, which were generated using the “calibration” function from the “rmda” package and the “rms” package, respectively."], "order_basis": "analysis_only", "id": "assay:1"}]}