Comprehensive analysis of a cuproptosis-related ceRNA network implicates a potential endocrine therapy resistance mechanism in ER-positive breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37143115", "kind": "science-record", "revision": 4, "content_hash": "sha256:30df60561dc99de957d33657b56f3201513c19a999dc236d25a4969895cf576a", "created_at": "2026-11-01T19:01:48Z", "updated_at": "2026-11-01T22:51:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of a cuproptosis-related ceRNA network implicates a potential endocrine therapy resistance mechanism in ER-positive breast cancer.", "doi": "10.1186/s12920-023-01511-0", "pmid": "37143115", "authors": ["Dongni Zhang", "Wenping Lu", "Zhili Zhuo", "Yanan Wang", "Weixuan Zhang", "Mengfan Zhang"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: While adjuvant endocrine therapy (ET) may decrease the mortality rate of estrogen receptor-positive (ER+) breast cancer (BC), the likelihood of relapse and metastasis due to ET resistance remains high. Cuproptosis is a recently discovered regulated cell death (RCD), whose role in tumors has yet to be elucidated. Thus, there is a need to study its specific regulatory mechanism in resistance to ET in BC, to identify novel therapeutic targets.\nMETHODS: The prognostic cuproptosis-related genes (CRGs) in ER+ BC were filtered by undergoing Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses in TCGA-BRCA, and a CRGs risk signature was constructed using the correlation coefficient. Immune infiltration analysis, immune function analysis, tumor microenvironment (TME) analysis, immune checkpoint analysis, immunotherapy response analysis, drug sensitivity analysis, and pathway activation analysis were carried out among the high- and low-risk groups in turn. The central CRG of cuproptosis in ER+ BC resistance to ET was acquired through the intersection of protein interaction network (PPI) analysis, genes differentially expressed (DEGs) between human BC cells LCC9 and MCF-7 (GSE159968), and CRGs with prognostic significance in TCGA-BRCA ER+ BC. The miRNAs upstream of the core CRGs were predicted based on the intersection of 4 databases, miRDB, RNA22, miRWalk, and RNAlnter. Candidate miRNAs consisted of the intersection of predicted miRNAs and miRNAs differentially expressed in the LCC9 and MCF-7 cell lines (GSE159979). Candidate lncRNAs were the intersection of the differential lncRNAs from the LCC9 and MCF-7 cell lines and the survival-related lncRNAs obtained from a univariate Cox regression analysis. Pearson's correlation analysis was performed between mRNA-miRNA, miRNA-lncRNA, and mRNA-lncRNA expression separately.\nRESULTS: We constructed A risk signature of 4-CRGs to predict the prognosis of ER+ BC in TCGA-BRCA, a risk score = DLD*0.378 + DBT*0.201 + DLAT*0.380 + ATP7A*0.447 was used as the definition of the formula. There were significant differences between the high- and low-risk groups based on the risk score of 4-CRGs in aspects of immune infiltration, immune function, expression levels of immune checkpoint genes, and signaling pathways. DLD was determined to be the central CRG of cuproptosis in ER+ BC resistance to ET through the intersection of the PPI network analysis, DEGs between LCC9 and MCF-7 and 4-CRGs. Two miRNAs hsa-miR-370-3p and hsa-miR-432-5p were found taking DLD mRNA as a target, and the lncRNA C6orf99 has been hypothesized to be a competitive endogenous RNA that regulates DLD mRNA expression by sponging off hsa-miR-370-3p and hsa-miR-432-5p.\nCONCLUSION: This study built a prognostic model based on genes related to cuproptosis in ER+ BC. We considered DLD to be the core gene associated with resistance to ET in ER+ BC via copper metabolism. The search for promising therapeutic targets led to the establishment of a cuproptosis-related ceRNA network C6orf99/hsa-miR-370-3p and hsa-miR-432-5p/DLD.", "claims": [{"id": "claim-method", "statement": "Prognostic CRGs in ER+ BC were filtered by undergoing Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses through the use of “survival” (Version: 3.2-13) and “survminer” (Version: 0.4.9) and “glmnet” [23] (Version: 4.1-3) package, the risk score of each patient was calculated by the formula Risk score = Ʃ (βi × Expi), in which, “βi” represents the LASSO correlation coefficient of gene “i”, while “Expi” represents gene “i”’s expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37143115", "locator": "sections[4] \"Establishment of a cuproptosis-related prognostic signature in ER+ BC\" paragraph 2", "quote": "Prognostic CRGs in ER+ BC were filtered by undergoing Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses through the use of “survival” (Version: 3.2-13) and “survminer” (Version: 0.4.9) and “glmnet” [23] (Version: 4.1-3) package, the risk score of each patient was calculated by the formula Risk score = Ʃ (βi × Expi), in which, “βi” represents the LASSO correlation coefficient of gene “i”, while “Expi” represents gene “i”’s expression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the TCGA-BRCA repository (https://portal.gdc.cancer.gov/projects/TCGA-BRCA) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/). 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The accession numbers of GEO datasets are GSE159968, GSE159979, and GSE17705."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed A risk signature of 4-CRGs to predict the prognosis of ER+ BC in TCGA-BRCA, a risk score = DLD*0.378 + DBT*0.201 + DLAT*0.380 + ATP7A*0.447 was used as the definition of the formula.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37143115", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We constructed A risk signature of 4-CRGs to predict the prognosis of ER+ BC in TCGA-BRCA, a risk score = DLD*0.378 + DBT*0.201 + DLAT*0.380 + ATP7A*0.447 was used as the definition of the formula."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed A risk signature of 4-CRGs to predict the prognosis of ER+ BC in TCGA-BRCA, a risk score = DLD*0.378 + DBT*0.201 + DLAT*0.380 + ATP7A*0.447 was used as the definition of the formula.", "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:37143115"], "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. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37143115", "data:geo:GSE159968"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE159968"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:38:42Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 17562, "completion_tokens": 3961, "cached_tokens": 188}}, "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:37143115", "type": "paper", "label": "pmid:37143115"}, {"id": "source:pmid:37143115", "type": "manuscript", "label": "PMID 37143115 full text"}, {"id": "data:geo:GSE159968", "type": "dataset", "label": "GEO GSE159968"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37143115", "type": "paper", "label": "pmid:37143115"}, {"id": "source:pmid:37143115", "type": "manuscript", "label": "PMID 37143115 full text"}, {"id": "data:geo:GSE159968", "type": "dataset", "label": "GEO GSE159968"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37143115", "type": "extraction", "used": ["source:pmid:37143115"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE159968", "type": "accession_resolution", "used": ["data:geo:GSE159968"], "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:37143115", "parent_hash": "sha256:df59f1c9c14ea379d3663ed90dc84a89b090b4d55e2465c8b16339947e5c0718", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE159968 licence not read", "asset:data:geo:GSE159968 not downloaded", "asset:data:geo:GSE159979 licence not read", "asset:data:geo:GSE159979 not downloaded", "asset:data:geo:GSE17705 licence not read", "asset:data:geo:GSE17705 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["essed (DEGs) between human BC cells LCC9 and MCF-7 (GSE159968), and CRGs with prognostic signif", "NAs differentially expressed in the LCC9 and MCF-7 cell lines (GSE159979). Candidate lncRNAs we", "f the differential lncRNAs from the LCC9 and MCF-7 cell lines and the survival-related lncRNAs", "PPI network analysis, DEGs between LCC9 and MCF-7 and 4-CRGs. Two miRNAs hsa-miR-370-3p and hs"], "order_basis": "paper_text_order:1337", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Prognostic CRGs in ER+ BC were filtered by undergoing Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses through the use of “survival” (Version: 3.2-13) and “survminer” (Version: 0.4.9) and “glmnet” [23] (Version: 4.1-3) package, the risk score of each patient was calculated by the formula Risk score = Ʃ (βi × Expi), in which, “βi” represents the LASSO correlation coefficient of gene “i”, while “Expi” represents gene “i”’s expression."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}