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Integrative 5-Methylcytosine Modification Immunologically Reprograms Tumor Microenvironment Characterizations and Phenotypes of Clear Cell Renal Cell Carcinoma

Wenhao Xu, Wenkai Zhu, Xi Tian, Wangrui Liu, Yuanyuan Wu, Aihetaimujiang Anwaier, Jiaqi Su, Shiyin Wei, Yuanyuan Qu, Hailiang Zhang, Dingwei Ye

FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY(2021)

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摘要
The tumor microenvironment (TME) affects the biologic malignancy of clear cell renal cell carcinoma (ccRCC). The influence of the 5-methylcytosine (m(5)C) epigenetic modification on the TME is unknown. We comprehensively assessed m(5)C modification patterns of 860 ccRCC samples (training, testing, and real-world validation cohorts) based on 17 m(5)C regulators and systematically integrated the modification patterns with TME cell-infiltrating characterizations. Our results identified distinct m(5)C modification clusters with gradual levels of immune cell infiltration. The distinct m(5)C modification patterns differ in clinicopathological features, genetic heterogeneity, patient prognosis, and treatment responses of ccRCC. An elevated m(5)C score, characterized by malignant biologic processes of tumor cells and suppression of immunity response, implies an immune-desert TME phenotype and is associated with dismal prognosis of ccRCC. Activation of exhausted T cells and effective immune infiltration were observed in the low m(5)C score cluster, reflecting a noninflamed and immune-excluded TME phenotype with favorable survival and better responses to immunotherapy. Together, these findings provide insights into the regulation mechanisms of DNA m(5)C methylation modification patterns on the tumor immune microenvironment. Comprehensive assessment of tumor m(5)C modification patterns may enhance our understanding of TME cell-infiltrating characterizations and help establish precision immunotherapy strategies for individual ccRCC patients.
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关键词
clear cell renal cell carcinoma,5-methylcytosine,tumor microenvironment,renal cell carcinoma (RCC) clear cell renal cell carcinoma (CCRCC),immune checkpoint therapies,prognosis,machine learning algorithm
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