A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38341588", "kind": "science-record", "revision": 4, "content_hash": "sha256:b5420ab0efca9a63e7fa97b32eaeeec8b69af377a1f48aa59bd459c9176a543c", "created_at": "2026-11-01T19:20:16Z", "updated_at": "2026-11-01T22:53:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC.", "doi": "10.1186/s12935-024-03267-8", "pmid": "38341588", "authors": ["Yijie Tang", "Tianyi Wang", "Qixuan Li", "Jiahai Shi"], "journal": "Cancer cell international", "year": 2024, "abstract": "BACKGROUND: Cuproptosis-related genes (CRGs) are associated with lung adenocarcinoma. However, the links between CRGs and non-small-cell lung cancer (NSCLC) are not clear. In this study, we aimed to develop two cuproptosis models and investigate their correlation with NSCLC in terms of clinical features and tumor microenvironment.\nMETHODS: CRG expression profiles and clinical data from NSCLC and normal tissues was obtained from GEO (GSE42127) and TCGA datasets. Molecular clusters were classified into three patterns based on CRGs and cuproptosis cluster-related specific differentially expressed genes (CRDEGs). Then, two clinical models were established. First, a prognostic score model based on CRDEGs was established using univariate/multivariate Cox analysis. Then, through principal component analysis, a cuproptosis score model was established based on prognosis-related genes acquired via univariate analysis of CRDEGs. NSCLC patients were divided into high/low risk groups.\nRESULTS: Eighteen CRGs were acquired, all upregulated in tumor tissues, 15 of which significantly (P < 0.05). Among the three CRG clusters, cluster B had the best prognosis. In the CRDEG clusters, cluster C had the best survival. In the prognostic score model, the high-risk group had worse prognosis, higher tumor mutation load, and lower immune infiltration while in the cuproptosis score model, a high score represented better survival, lower tumor mutation load, and high-level immune infiltration.\nCONCLUSIONS: The cuproptosis score model and prognostic score model may be associated with NSCLC prognosis and immune microenvironment. These novel findings on the progression and immune landscape of NSCLC may facilitate the provision of more personalized immunotherapy interventions for NSCLC patients.", "claims": [{"id": "claim-method", "statement": "The PCA method and lasso cox regression analysis were used to build the two models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38341588", "locator": "sections[16] \"Statistical analysis\" paragraph 2", "quote": "The PCA method and lasso cox regression analysis were used to build the two models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the present study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and GEO repository (GSE42127) (https://www.ncbi.nlm.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38341588", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The datasets generated and analyzed during the present study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and GEO repository (GSE42127) (https://www.ncbi.nlm.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) datasets we first investigated the expression of CRGs in 1282 NSCLC samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38341588", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "Based on the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) datasets we first investigated the expression of CRGs in 1282 NSCLC samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) datasets we first investigated the expression of CRGs in 1282 NSCLC samples.", "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:38341588"], "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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