Comprehensive analysis of cuproptosis and copper homeostasis genotyping and related immune land scape in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37783723", "kind": "science-record", "revision": 5, "content_hash": "sha256:4545c293107ff4ae871a6eb785a30135887d018b1203ded6124dd497819df5b0", "created_at": "2026-11-01T19:02:38Z", "updated_at": "2026-11-01T22:52:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of cuproptosis and copper homeostasis genotyping and related immune land scape in lung adenocarcinoma.", "doi": "10.1038/s41598-023-43795-3", "pmid": "37783723", "authors": ["Dayuan Luo", "Xiang Wang", "Wei Feng"], "journal": "Scientific reports", "year": 2023, "abstract": "Cuproptosis is a manner of cell death which is related to the homeostasis of copper ions in the cellular environment and is expected to open a new direction of anti-tumor therapy. However, the studies on cuproptosis and copper homeostasis in lung adenocarcinoma (LUAD) are still limited. In this study, we identified new cuproptosis and copper homeostasis related genes (CHRGs) which were effective in stratifying genotyping clusters with survival differences based on transcriptomic data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219. Comprehensive bioinformatics analysis revealed these genes are potential regulators of modulating immunotherapy efficacy, drug resistance, tumor microenvironment infiltration, and tumor mutation patterns. Lastly, the scRNA-seq datasets GSE183219 and GSE203360 offers the evidences that CHRGs signature are mainly distributed in cancer epithelial cells, real time quantitative polymerase chain reaction (RT-qPCR) also confirmed the differential expression of these genes between normal lung cell line and lung adenocarcinoma cell lines. Collectively, our findings revealed new cuproptosis and copper homeostasis related genotyping clusters and genes which may play important roles in predicting prognosis, influencing tumor microenvironment and drug efficacy in LUAD patients.", "claims": [{"id": "claim-method", "statement": "Survival analysis between different subtypes and subgroups were based on the Kaplan–Meier analysis and two-sided log-rank test with R package ‘Survminer’.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37783723", "locator": "sections[13] \"Identification of cuproptosis subtypes and copper homeostasis subgroups\" paragraph 2", "quote": "Survival analysis between different subtypes and subgroups were based on the Kaplan–Meier analysis and two-sided log-rank test with R package ‘Survminer’."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The analysis data in the paper can be found in TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena database (http://xena.ucsc.edu/), and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37783723", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The analysis data in the paper can be found in TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena database (http://xena.ucsc.edu/), and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37783723", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219.", "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:37783723"], "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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The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The analysis data in the paper can be found in TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena database (http://xena.ucsc.edu/), and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). 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The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The analysis data in the paper can be found in TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena database (http://xena.ucsc.edu/), and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). 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The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The analysis data in the paper can be found in TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena database (http://xena.ucsc.edu/), and MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The secondary research data were obtained from Metascape (www.metascape.org/), TIDE database (http://tide.dfci.harvard.edu/), TCIA database (https://tcia.at/home), GDSC database (https://www.cancerrxgene.org/), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1465989", "GSM1465990", "GSM1465991", "GSM1465992", "GSM1465993", "GSM1465994", "GSM1465995", "GSM1465996", "GSM1465997", "GSM1465998", "GSM1465999", "GSM748053", "GSM748054", "GSM748055", "GSM748056", "GSM748057", "GSM748058", "GSM748059", "GSM748060", "GSM748061", "GSM748062", "GSM748063", "GSM748064", "GSM748065", "GSM748066", "GSM748067", "GSM748068", "GSM748069", "GSM748070", "GSM748071", "GSM748072", "GSM748073", "GSM748074", "GSM748075", "GSM748076", "GSM748077", "GSM748078", "GSM748079", "GSM748080", "GSM748081", "GSM748083", "GSM748084", "GSM748085", "GSM748086", "GSM748087", "GSM748088", "GSM748089", "GSM748090", "GSM748091", "GSM748092", "GSM748093", "GSM748094", "GSM748095", "GSM748096", "GSM748097", "GSM748098", 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All the obtained data in this study is publicly available and complied with the statements and norms of the official websites, there were no ethical problems or copyright conflicts."], "basis": "The paper names GSE30219, GSE183219, GSE203360 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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Lastly, the scRNA-seq datasets GSE183219 and GSE203360 offers the"], "order_basis": "paper_text_order:1113", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["time quantitative polymerase chain reaction (RT-qPCR) also confirmed the differential expression", "e quantitative polymerase chain reaction (RT-qPCR) also confirmed the differential expression", "Cell culture and real time quantitative PCR (RT-qPCR) Human lung adenocarcinoma cell line PC-9 (C", "into cDNA. β-actin was used as a reference, RT-qPCR was utilized to detect the expression of mRN"], "order_basis": "paper_text_order:1299", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["related to immune checkpoint, cell cycle and apoptosis based on Spearman analysis. 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