Identification of a claudin-low subtype in clear cell renal cell carcinoma with implications for the evaluation of clinical outcomes and treatment efficacy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36479115", "kind": "science-record", "revision": 3, "content_hash": "sha256:b63c65d36254dcb2489ae32a1069e409a7731d32c5206fb236d3d01d699dbbc8", "created_at": "2026-11-01T19:00:58Z", "updated_at": "2026-11-01T22:08:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a claudin-low subtype in clear cell renal cell carcinoma with implications for the evaluation of clinical outcomes and treatment efficacy.", "doi": "10.3389/fimmu.2022.1020729", "pmid": "36479115", "authors": ["Cuijian Zhang", "Yifan Li", "Jinqin Qian", "Zhenpeng Zhu", "Cong Huang", "Zhisong He", "Liqun Zhou", "Yanqing Gong"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: In bladder and breast cancer, the claudin-low subtype is widely identified, revealing a distinct tumor microenvironment (TME) and immunological feature. Although we have previously identified individual claudin members as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC), the existence of an intrinsic claudin-low subtype and its interplay with TME and clinical outcomes remains unclear.\nMethods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)- kidney clear cell carcinoma (KIRC) cohort and E-MTAB-1980 were derived as the training and validation cohorts, respectively. In addition, GSE40435, GSE53757, International Cancer Genome Consortium (ICGC) datasets, and RNA-sequencing data from local ccRCC patients were utilized as validation cohorts for claudin clustering based on silhouette scores. Using weighted correlation network analysis (WGCNA) and multiple machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), CoxBoost, and random forest, we constructed a claudin-TME related (CTR) risk signature. Furthermore, the CTR associated genomic characteristics, immunity, and treatment sensitivity were evaluated.\nResults: A claudin-low phenotype was identified and associated with an inferior survival and distinct TME and cancer immunity characteristics. Based on its interaction with TME, a risk signature was developed with robust prognostic prediction accuracy. Moreover, we found its association with a claudin-low, stem-like phenotype and advanced clinicopathological features. Intriguingly, it was also effective in kidney chromophobe and renal papillary cell carcinoma. The high CTR group exhibited genomic characteristics similar to those of claudin-low phenotype, including increased chromosomal instability (such as deletions at 9p) and risk genomic alterations (especially BAP1 and SETD2). In addition, a higher abundance of CD8 T cells and overexpression of immune checkpoints, such as LAG3, CTLA4 and PDCD1, were identified in the high CTR group. Notably, ccRCC patients with high CTR were potentially more sensitive to immune checkpoint inhibitors; their counterparts could have more clinical benefits when treated with antiangiogenic drugs, mTOR, or HIF inhibitors.\nConclusion: We comprehensively evaluated the expression features of claudin genes and identified a claudin-low phenotype in ccRCC. In addition, its related signature could robustly predict the prognosis and provide guide for personalizing management strategies.", "claims": [{"id": "claim-method", "statement": "Using weighted correlation network analysis (WGCNA) and multiple machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), CoxBoost, and random forest, we constructed a claudin-TME related (CTR) risk signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36479115", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using weighted correlation network analysis (WGCNA) and multiple machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), CoxBoost, and random forest, we constructed a claudin-TME related (CTR) risk signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data presented in the study are deposited in the China National Center for Bioinformation/National Genomics Data Center of China (https://ngdc.cncb.ac.cn/gsahuman/) accession number HRA003416.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36479115", "locator": "sections[31] \"Data availability statement\" paragraph 2", "quote": "The data presented in the study are deposited in the China National Center for Bioinformation/National Genomics Data Center of China (https://ngdc.cncb.ac.cn/gsahuman/) accession number HRA003416."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CTR score predicted survival at 1-, 2-, 3-, 4-, and 5-years with AUC values of 0.81, 0.78, 0.78, 0.77, and 0.77, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36479115", "locator": "sections[23] \"3.4 Evaluation and validation of the prognostic predictive accuracy of CTR signature\" paragraph 2", "quote": "The AUC of the CTR score for predicting survival at 1-, 2-, 3-, 4-, and 5- year was 0.81, 0.78, 0.78, 0.77, and 0.77, respectively ( Figure 5B )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The CTR score predicted survival at 1-, 2-, 3-, 4-, and 5-years with AUC values of 0.81, 0.78, 0.78, 0.77, and 0.77, 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:36479115"], "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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