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EZH2调控的免疫风险评分预后模型可预测透明细胞肾细胞癌的预后。

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

作者信息

Xu Shan, Ma Bohan, Feng Xiaoyu, Yao Chen, Jian Yanlin, Chen Yule, Wang Xinyang, Xie Hongjun, Li Lei

机构信息

Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Oncology Research Laboratory, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China.

出版信息

Transl Androl Urol. 2023 Jan 30;12(1):71-82. doi: 10.21037/tau-22-817. Epub 2023 Jan 1.

DOI:10.21037/tau-22-817
PMID:36760869
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9906105/
Abstract

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.

摘要

背景

zeste 同源物 2 增强子(EZH2)在肿瘤微环境(TME)中起重要作用,EZH2 在塑造 CD8 T 细胞命运和功能的表观遗传格局方面发挥作用,尤其在癌症方面。在此,EZH2 高表达总是导致 CD8 T 细胞浸润减少。然而,据报道透明细胞肾细胞癌(ccRCC)是一种“热”肿瘤,其 EZH2 表达却相互矛盾地升高。我们的目标是构建一个 EZH2 调节的免疫风险评分预后模型来预测 ccRCC 的预后,并为临床 EZH2 抑制剂在通过免疫疗法微调 T 细胞反应方面提供前景。

方法

我们下载并分析了癌症基因组图谱(TCGA)、癌细胞系百科全书(CCLE)、TISIDB 数据库以及 WebGestalt 中 ccRCC 患者、EZH2 相关的肿瘤浸润淋巴细胞和免疫调节因子的数据。下载了 R 包“limma”、“BiocManager”和“preprocessCore”等,以准备 CIBERSORT 文件、免疫细胞热图、多变量 Cox 模型和生存分析。在 R studio 中通过受试者工作特征(ROC)和曲线下面积(AUC)分析计算 EZH2 调节的免疫风险模型的预后能力。

结果

EZH2 在 ccRCC 中高表达且与不良预后相关。然而,高表达的 EZH2 与“冷”肿瘤无关。在由 EZH2 显著调节的 49 种免疫调节因子中,森林图显示 26 种免疫调节因子特征与总生存期独立相关。EZH2 调节的免疫风险评分预后模型是一个独立的预后因素(AUC = 0.816),特别是在 ccRCC 总生存期预测中与临床病理参数相结合时。

结论

EZH2 调节的免疫风险评分预后模型是一个独立的预后因素,具有良好的准确性和可预测性,并可为临床领域提供实验数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/6fc263faa8e3/tau-12-01-71-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/1b48dff67f6b/tau-12-01-71-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/8fece33bbd25/tau-12-01-71-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/41d69f61fe98/tau-12-01-71-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/20d43d2dd627/tau-12-01-71-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/1b01b68dd7ec/tau-12-01-71-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/6fc263faa8e3/tau-12-01-71-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/1b48dff67f6b/tau-12-01-71-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/8fece33bbd25/tau-12-01-71-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/41d69f61fe98/tau-12-01-71-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/20d43d2dd627/tau-12-01-71-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/1b01b68dd7ec/tau-12-01-71-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c009/9906105/6fc263faa8e3/tau-12-01-71-f6.jpg

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