Development of a novel copper metabolism-related risk model to predict prognosis and tumor microenvironment of patients with stomach adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37284310", "kind": "science-record", "revision": 3, "content_hash": "sha256:484aac3572760272b7646706611a31592fc3ce954023d9a7f65f784a884a8840", "created_at": "2026-11-01T19:02:00Z", "updated_at": "2026-11-01T22:09:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a novel copper metabolism-related risk model to predict prognosis and tumor microenvironment of patients with stomach adenocarcinoma.", "doi": "10.3389/fphar.2023.1185418", "pmid": "37284310", "authors": ["Dongjie Sun", "Haiying Zhang", "Chi Zhang"], "journal": "Frontiers in pharmacology", "year": 2023, "abstract": "Background: Stomach adenocarcinoma (STAD) is the fourth highest cause of cancer mortality worldwide. Alterations in copper metabolism are closely linked to cancer genesis and progression. We aim to identify the prognostic value of copper metabolism-related genes (CMRGs) in STAD and the characteristic of the tumor immune microenvironment (TIME) of the CMRG risk model. Methods: CMRGs were investigated in the STAD cohort from The Cancer Genome Atlas (TCGA) database. Then, the hub CMRGs were screened out with LASSO Cox regression, followed by the establishment of a risk model and validated by GSE84437 from the Expression Omnibus (GEO) database. The hub CMRGs were then utilized to create a nomogram. TMB (tumor mutation burden) and immune cell infiltration were investigated. To validate CMRGs in immunotherapy response prediction, immunophenoscore (IPS) and IMvigor210 cohort were employed. Finally, data from single-cell RNA sequencing (scRNA-seq) was utilized to depict the properties of the hub CMRGs. Results: There were 75 differentially expressed CMRGs identified, 6 of which were linked with OS. 5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model. High-risk patients had a shorter life expectancy than those low-risk. The risk score independently predicted STAD survival through univariate and multivariate Cox regression analyses, with ROC calculation generating the highest results. This risk model was linked to immunocyte infiltration and showed a good prediction performance for STAD patients' survival. Furthermore, the high-risk group had lower TMB and somatic mutation counters and higher TIDE scores, but the low-risk group had greater IPS-PD-1 and IPS-CTLA4 immunotherapy prediction, indicating a higher immune checkpoint inhibitors (ICIs) response, which was corroborated by the IMvigor210 cohort. Furthermore, those with low and high risk showed differential susceptibility to anticancer drugs. Based on CMRGs, two subclusters were identified. Cluster 2 patients had superior clinical results. Finally, the copper metabolism-related TIME of STAD was concentrated in endothelium, fibroblasts, and macrophages. Conclusion: CMRG is a promising biomarker of prognosis for patients with STAD and can be used as a guide for immunotherapy.", "claims": [{"id": "claim-method", "statement": "Hub CRMGs were screened out by LASSO Cox regression using the “glmnet” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37284310", "locator": "sections[4] \"CMRG identification\" paragraph 2", "quote": "Hub CRMGs were screened out by LASSO Cox regression using the “glmnet” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37284310", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed a five-CMRG prognostic risk model for STAD using TCGA data and validated it in the GSE84437 cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37284310", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed a five-CMRG prognostic risk model for STAD using TCGA data and validated it in the GSE84437 cohort.", "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:37284310"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37284310", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2023.1185418", "sha256": 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