Identification and characterization of cuproptosis related gene subtypes through multi-omics bioinformatics analysis in breast cancer.

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{"pmcid": "PMC11825418", "section_index": 13, "section_title": "The expression levels of CRGs in TCGA-BRCA and GEO cohort", "row_count": 20, "column_count": 2}, "table": {"label": "Table 1", "caption": "Official symbol and full name of 19 cuproptosis related genes", "rows": [["Official symbol of cuproptosis related gene", "Official full name"], ["NFE2L2", "NFE2 like BZIP transcription factor 2"], ["NLRP3", "NLR family pyrin domain containing 3"], ["ATP7B", "ATPase copper transporting beta"], ["ATP7A", "ATPase copper transporting alpha"], ["SLC31A1", "Solute carrier family 31 member 1"], ["FDX1", "Ferredoxin 1"], ["LIAS", "Lipoic acid synthetase"], ["LIPT1", "Lipoyltransferase 1"], ["LIPT2", "Lipoyl(Octanoyl) transferase 2"], ["DLD", "Dihydrolipoamide dehydrogenase"], ["DLAT", "Dihydrolipoamide S-Acetyltransferase"], ["PDHA1", "Pyruvate dehydrogenase E1 subunit alpha 1"], ["PDHB", "Pyruvate dehydrogenase E1 subunit beta"], ["MTF1", "Metal regulatory transcription factor 1"], ["GLS", "Glutaminase"], ["CDKN2A", "Cyclin dependent kinase inhibitor 2A"], ["DBT", "Dihydrolipoamide branched chain transacylase E2"], ["GCSH", "Glycine cleavage system protein H"], ["DLST", "Dihydrolipoamide S-Succinyltransferase"]], "row_count": 20, "column_count": 2, "numeric_cells": 10, "header_rows": 1, "locator": "section[13]/table-wrap[1]#Tab1"}}], "gaps": ["asset:source:pmid:39945992 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:39945992", "asset:table:pmid:39945992:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39945992"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "26037db58c37d316b701c33e54a8e1b0e424a232dcbd1e4cc31bf967ee5844cf", "source_hash": "20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd", "raw_response_sha256": "sha256:34fa5c36348be1fc50809c609239e0fa9d75cafbbdf18582f38046b809c7b866", "raw_response_chars": 4286}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "17103cb444d7ee60886cd0af3bf9060b62dcce553c6036027f8aa84689930fcd", "source_hash": "20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd", "raw_response_sha256": "sha256:04c3b23e1935fce84dadf27ab26b54643cbe41651d9ffd729070000c3a5baa04", "raw_response_chars": 82330}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "2b9b76402af02ca6b8ce19338a156ca49fbafc8dc3f6193db9bd028059577504", "source_hash": "20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd", "raw_response": "{\"parent_value\":2,\"unit\":\"distinct clusters of CRGs\",\"evidence\":\"Through this study appraisal, two distinct clusters of CRGs were identified in breast cancer.\",\"target_result\":\"Two distinct clusters of cuproptosis-related genes were identified in breast cancer.\",\"number_stated\":true}", "reading": {"parent_value": 2, "unit": "distinct clusters of CRGs", "evidence": "Through this study appraisal, two distinct clusters of CRGs were identified in breast cancer.", "target_result": "Two distinct clusters of cuproptosis-related genes were identified in breast cancer.", "number_stated": true, "source_locator": "chars 8926-9019 of source sha256 20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd"}, "recorded_at": "2026-10-31T15:24:50.125574+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "cbee315a1c5a40aa6547dc384994cabd2bb9ea22e8508ca6bdf4e6c7b6b3717d", "source_hash": "20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result headline main finding stated. Registered target vague \"headline numerical result the paper states for its main finding\". Paper title/abstract says identified two subtypes, CRGcluster S2 poorer prognosis. Main headline numerical result likely 2 CRGclusters. Need report headline number only value paper states for registered target, unit paper prints. Evidence exact contiguous quote containing number; attribution clause whole. Candidate: \"We performed consistent clustering analysis to divide the tumor cases of the merged cohort separated into two separate clusters, namely CRGcluster S1 and CRGcluster S2 (Fig. 4A–C).\" This states two clusters. Or abstract: \"We identified two subtypes, where CRGcluster S2 exhibits a poorer prognosis compared to CRGcluster S1.\" Exact quote, clear. Number word \"two\", parent_value number 2. Unit? Paper prints \"two subtypes\" so unit \"subtypes\" perhaps. User says headline number only, value paper states, unit must unit paper prints. Could use \"subtypes\". Evidence exact contiguous quote. target_result perhaps \"Two breast cancer subtypes/CRGclusters were identified.\" But they request headline number only maybe target_result wording perhaps number only? Schema includes target_result. Need set e.g. \"Two subtypes\" or \"2 subtypes\". Parent value 2, unit \"subtypes\", evidence quote. number_stated true.\n\nNeed distinguish another main headline numerical result perhaps 1070 DEGs? Registered target \"the headline numerical result the paper states for its main finding\" likely ask extraction benchmark where target maybe main finding numerical result. Abstract headline: \"We identified two subtypes\". So yes.\n\nNeed exact quote from source. \"We identified two subtypes, where CRGcluster S2 exhibits a poorer prognosis compared to CRGcluster S1.\" This is contiguous and target number. Unit printed \"subtypes\". target_result likely \"Two subtypes\" perhaps \"2 subtypes.\" They say Report headline number only. JSON necessarily fields. Could target_result be \"2 subtypes\". evidence exact. Ensure no extra.</think>\n\n{\"parent_value\":2,\"unit\":\"subtypes\",\"evidence\":\"We identified two subtypes, where CRGcluster S2 exhibits a poorer prognosis compared to CRGcluster S1.\",\"target_result\":\"2 subtypes\",\"number_stated\":true}", "reading": {"parent_value": 2, "unit": "subtypes", "evidence": "We identified two subtypes, where CRGcluster S2 exhibits a poorer prognosis compared to CRGcluster S1.", "target_result": "2 subtypes", "number_stated": true, "source_locator": "chars 1013-1115 of source sha256 20116a68f0e2b29729532877db32051123860bbb355df8747208e461cd32f3dd"}, "recorded_at": "2026-10-31T15:24:52.998800+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "Two breast cancer CRG subtypes were identified: CRGcluster S1 and CRGcluster S2, with CRGcluster S2 exhibiting a poorer prognosis than CRGcluster S1.", "minimax_alt": "Two breast cancer subtypes were identified: CRGcluster S1 and CRGcluster S2.", "decisive": false}, "analysis_methods": {"minimax": ["Consensus cluster analysis using the ConsensusClusterPlus package", "Principal component analysis", "Kaplan–Meier survival analysis with log-rank testing", "Univariate Cox survival analysis", "Wilcoxon difference analysis", "Spearman correlation analysis", "Functional enrichment analysis of differentially expressed genes", "Tumor mutation burden analysis using maftools", "Microsatellite instability analysis using cBioPortalData", "ssGSEA immune-cell infiltration analysis", "CIBERSORT immune-cell infiltration analysis", "ESTIMATE immune and stromal score analysis", "Drug-sensitivity analysis"], "minimax_alt": ["Consensus cluster analysis using the ConsensusClusterPlus package (consensus matrix k = 2)", "Principal component analysis (PCA)", "Differential survival analysis using Kaplan–Meier survival analysis with log-rank testing", "Univariate Cox survival analysis", "Wilcoxon difference testing", "Spearman correlation analysis", "GO and KEGG functional enrichment analysis using clusterProfiler and org.Hs.eg.db", "Tumor mutation burden analysis using maftools", "Microsatellite instability scoring using cBioPortalData", "Cancer cell stemness index analysis based on DNA methylation patterns", "Immune infiltration analysis using ssGSEA", "Immune infiltration analysis using CIBERSORT", "ESTIMATE algorithm for stromal, immune, and ESTIMATE scores", "Drug sensitivity testing (specific statistical procedure not stated)"], "decisive": false}, "statistical_method": {"minimax": "Consensus cluster analysis", "minimax_alt": "Consensus cluster analysis (ConsensusClusterPlus; 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