ArticleFrontiers in immunology2022
Identification of a claudin-low subtype in clear cell renal cell carcinoma with implications for the evaluation of clinical outcomes and treatment efficacy.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 24 citations in OpenAlex.
- ERH Impacts Patient Prognosis and Tumor Immune Microenvironment: A Pan-Cancer Analysis.Combinatorial chemistry & high throughput screening · 2025Article
- A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers.Cell communication and signaling : CCS · 2024Article
- The role of claudins in homeostasis.Nature reviews. Nephrology · 2023Review
- Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach.Functional & integrative genomics · 2023Article
- Article
- The discovery of promising candidate biomarkers in kidney renal clear cell carcinoma: evidence from the in-depth analysis of high-throughput data.American journal of cancer research · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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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. Methods: 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. Results: 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 Conclusion: 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.
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