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EZH2-regulated immune risk score prognostic model predicts outcome of clear cell renal cell carcinoma

BACKGROUND: The enhancer of zeste homolog 2 (EZH2) plays an important role in the tumor microenvironment (TME), and EZH2 in shaping the epigenetic landscape of CD8(+) T cell fate and function, with a particular emphasis on cancer. Here, high EZH2 expression always leads to less CD8(+) T cell infiltr...

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Detalles Bibliográficos
Autores principales: Xu, Shan, Ma, Bohan, Feng, Xiaoyu, Yao, Chen, Jian, Yanlin, Chen, Yule, Wang, Xinyang, Xie, Hongjun, Li, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906105/
https://www.ncbi.nlm.nih.gov/pubmed/36760869
http://dx.doi.org/10.21037/tau-22-817
Descripción
Sumario:BACKGROUND: The enhancer of zeste homolog 2 (EZH2) plays an important role in the tumor microenvironment (TME), and EZH2 in shaping the epigenetic landscape of CD8(+) T cell fate and function, with a particular emphasis on cancer. Here, high EZH2 expression always leads to less CD8(+) T cell infiltration. However, clear cell renal cell carcinoma (ccRCC) is reportedly a “hot” tumor, with contradictory high EZH2 expression. Our goal was to construct a EZH2-regulated immune risk score prognostic model to predict ccRCC outcomes, and provide a prospect of clinical EZH2 inhibitors in fine-tuning T cell responses with immune therapy. METHODS: We downloaded and analyzed The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), TISIDB database, and WebGestalt for ccRCC patients, EZH2-related tumor-infiltrating lymphocytes and immunomodulators. R packages “limma”, “BiocManager”, and “preprocessCore”, etc. were downloaded to prepare CIBERSORT files, immune cells heatmap, multivariable Cox model and survival analysis. The EZH2-regulated immune risk model’s prognostic ability was calculated by receiver operating characteristic (ROC) and area under the curve (AUC) analyses in R studio. RESULTS: EZH2 was highly expressed and related to poor outcome in ccRCC. However, high-expression EZH2 was not related to a “cool” tumor. Of the 49 immunomodulators significantly regulated by EZH2, forest plot showed 26 immunomodulators signatures independently associated with overall survival. The EZH2-regulated immune-risk score prognostic model was an independent prognostic factor (AUC =0.816), especially combined with clinicopathologic parameters in ccRCC overall survival prediction. CONCLUSIONS: The EZH2-regulated immune-risk score prognostic model was an independent prognostic factor, with good accuracy and predictability, and could provide experimental data to the clinical area.