Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37665670", "kind": "science-record", "revision": 3, "content_hash": "sha256:08b1bbf093a06ff1f7ae20bbcbeedb35a5b344b80d02ea4ade668aed24ab4b8f", "created_at": "2026-11-01T19:02:27Z", "updated_at": "2026-11-01T22:10:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma.", "doi": "10.18632/aging.205012", "pmid": "37665670", "authors": ["Yiping Zhang", "Kebing Chen", "Liyan Wang", "Juhui Chen", "Zhizhong Lin", "Yuanmei Chen", "Junqiang Chen", "Yu Lin", "Yuanji Xu", "Haiyan Peng"], "journal": "Aging", "year": 2023, "abstract": "Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.", "claims": [{"id": "claim-method", "statement": "To construct a prognostic signature and screen prognostic CRGs in the TCGA-ESCC dataset, we utilized the glmnet package [25] to perform Least absolute shrinkage and selection operator (LASSO) Cox regression [26].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37665670", "locator": "sections[7] \"Construction of a prognostic risk model based on CRGs\" paragraph 2", "quote": "To construct a prognostic signature and screen prognostic CRGs in the TCGA-ESCC dataset, we utilized the glmnet package [25] to perform Least absolute shrinkage and selection operator (LASSO) Cox regression [26]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We used the TCGABiolinks R package [14] to download the ESCC dataset (TCGA-ESCC) from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/) and analyzed it as the test set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37665670", "locator": "sections[3] \"Data download and arrangement\" paragraph 2", "quote": "We used the TCGABiolinks R package [14] to download the ESCC dataset (TCGA-ESCC) from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/) and analyzed it as the test set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used LASSO Cox regression to construct an 11-gene CRGs prognostic signature for ESCC from the TCGA-ESCC dataset, and the resulting risk model achieved 1-, 2-, and 3-year AUC values of 0.847, 0.958, and 1.000, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37665670", "locator": "sections[18] \"Construction and prognostic risk model based on 11 cuproptosis-related genes\" paragraph 2", "quote": "Based on the LASSO regression analysis, we used 11 CRGs, including ATP7B, CDKN2A, DLAT, DLD, FDX1, LIAS, LIPT1, MTF1, PDHA1, PDHB, and SLC31A1, according to the minimum-minimum criterion and optimal-minimum criterion."}, {"asset": "source:pmid:37665670", "locator": "sections[18] \"Construction and prognostic risk model based on 11 cuproptosis-related genes\" paragraph 2", "quote": "As shown in Figure 6E, the AUC values for 1-, 2-, and 3-year were 0.847, 0.958, and 1.000, respectively."}, {"asset": "source:pmid:37665670", "locator": "sections[3] \"Data download and arrangement\" paragraph 2", "quote": "We used the TCGABiolinks R package [14] to download the ESCC dataset (TCGA-ESCC) from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/) and analyzed it as the test set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used LASSO Cox regression to construct an 11-gene CRGs prognostic signature for ESCC from the TCGA-ESCC dataset, and the resulting risk model achieved 1-, 2-, and 3-year AUC values of 0.847, 0.958, and 1.000, respectively.", "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:37665670"], "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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