Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36082002", "kind": "science-record", "revision": 3, "content_hash": "sha256:db6c864eeb867cee44952584416ba2f01ff8afc18281c1c1363279cb0f7f96e8", "created_at": "2026-11-01T18:15:18Z", "updated_at": "2026-11-01T21:28:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis.", "doi": "10.3389/fgene.2022.949852", "pmid": "36082002", "authors": ["Xiao Guan", "Na Lu", "Jianping Zhang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Purpose: To explore the clinical significance of copper-dependent-related genes (CDRG) in female breast cancer (BC). Methods: CDRG were obtained by single-cell analysis of the GSE168410 dataset in the Gene Expression Omnibus (GEO) database. According to a 1:1 ratio, the Cancer Genome Atlas (TCGA) cohort was separated into a training and a test cohort randomly. Based on the training cohort, the prognostic model was built using COX and Lasso regression. The test cohort was used to validate the model. The GSE20685 dataset and GSE20711 dataset were used as two external validation cohorts to further validate the prognostic model. According to the median risk score, patients were classified as high-risk or low-risk. Survival analysis, immune microenvironment analysis, drug sensitivity analysis, and nomogram analysis were used to evaluate the clinical importance of this prognostic model. Results: 384 CDRG were obtained by single-cell analysis. According to the prognostic model, patients were classified as high-risk or low-risk in both cohorts. The high-risk group had a significantly worse prognosis. The area under the curve (AUC) of the model was around 0.7 in the four cohorts. The immunological microenvironment was examined for a possible link between risk score and immune cell infiltration. Veliparib, Selumetinib, Entinostat, and Palbociclib were found to be more sensitive medications for the high-risk group after drug sensitivity analysis. Conclusion: Our CDRG-based prognostic model can aid in the prediction of prognosis and treatment of BC patients.", "claims": [{"id": "claim-method", "statement": "Based on the training cohort, the prognostic model was built using COX and Lasso regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36082002", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Based on the training cohort, the prognostic model was built using COX and Lasso regression."}], "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:36082002", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE168410"], "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:GSE168410"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36082002", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.949852", "sha256": "sha256:b360037a504ae79064fc3908a96e8efdf6329a2e30863c2ab55f254347915e88", "bytes": 37286, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.949852", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9445252", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE168410", "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=GSE168410", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE168410", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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