As the most prevalent internal eukaryotic modification, N⁶-methyladenosine (m⁶A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m⁶A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m⁶A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m⁶A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature’s predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different (). The ROC curve also indicated that the risk signature had a decent predictive significance (). These results imply that the risk signature has a potential value for ccRCC treatment.

中文翻译：

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基于 N6-甲基腺苷 RNA 甲基化调节剂鉴定透明细胞肾细胞癌的新预后风险特征

作为最普遍的内部真核修饰，N ⁶ -甲基腺苷 (m ⁶ A) 由甲基转移酶安装，由去甲基酶去除，并被阅读器识别。然而，关于 m ⁶ A 在透明细胞肾细胞癌 (ccRCC) 中的作用的研究很少。在本研究中，我们研究了 TCGA 数据集中 ccRCC 的 RNA-seq 转录组数据，并使用生物信息学分析来检测 m ⁶ A RNA 甲基化调节因子与 ccRCC之间的关系。首先，我们比较了18 m ⁶的表达ccRCC 患者和正常组织中的 RNA 甲基化调节因子。然后，通过共识聚类将来自 ccRCC 患者的数据分为两个聚类。LASSO Cox 回归分析用于构建风险特征以预测 ccRCC 患者的预后。ROC 曲线、单变量 Cox 回归分析和多变量 Cox 回归分析用于验证该风险特征的预测能力。然后，我们通过随机抽样在内部验证了这个签名。最后，我们探讨了基因在一些常见途径中的特征中的作用。两个亚组之间的基因分布不同；第 2 组与性别有关，预后较差。选择 IGF2BP3、IGF2BP2、HNRNPA2B1 和 METTL14 来构建风险签名。高危组和低危组的总生存率有显着差异（）。ROC 曲线还表明风险特征具有良好的预测意义（）。这些结果意味着风险特征对于 ccRCC 治疗具有潜在价值。