Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37928426", "kind": "science-record", "revision": 3, "content_hash": "sha256:471529bc906058a9f28d0d4cce930b0c9884b4f73f7cc464c78785dbbc07765d", "created_at": "2026-11-01T19:02:49Z", "updated_at": "2026-11-01T22:10:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature.", "doi": "10.7150/jca.88467", "pmid": "37928426", "authors": ["Gang Liu", "Feifei Li", "Yuntian Ge", "Yaxing Shi", "Fang Ren", "Liancheng Zhu"], "journal": "Journal of Cancer", "year": 2023, "abstract": "Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.", "claims": [{"id": "claim-method", "statement": "Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37928426", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Most of the data sets used and/or analyzed during the current study are publicly available data from TCGA, ICGC, XENA, CheckMate025 cohort, and Gene Expression Omnibus (GEO) databases (GSE22541, GSE73731). All data of the independent cohort in the current study were available from the corresponding authors in a reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37928426", "locator": "sections[33] \"Availability of data and material\" paragraph 2", "quote": "Most of the data sets used and/or analyzed during the current study are publicly available data from TCGA, ICGC, XENA, CheckMate025 cohort, and Gene Expression Omnibus (GEO) databases (GSE22541, GSE73731). All data of the independent cohort in the current study were available from the corresponding authors in a reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A predictive risk signature comprising CD276, HLA-E, LGALS9, and TNFRSF18 was created and externally confirmed.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37928426", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A predictive risk signature comprising CD276, HLA-E, LGALS9, and TNFRSF18 was created and externally confirmed.", "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:37928426"], "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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