Department of Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Department of Clinical Laboratory, The First People's Hospital of Linhai, Taizhou, China.
J Immunol Res. 2021 Feb 12;2021:6617841. doi: 10.1155/2021/6617841. eCollection 2021.
As the most prevalent internal eukaryotic modification, N-methyladenosine (mA) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of mA 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 mA RNA methylation regulators and ccRCC. First, we compared the expression of 18 mA 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 ( = 7.47 - 12). The ROC curve also indicated that the risk signature had a decent predictive significance (AUC = 0.72). These results imply that the risk signature has a potential value for ccRCC treatment.
作为最普遍的内源性真核修饰,N6-甲基腺苷(m6A)由甲基转移酶进行安装,由去甲基酶进行移除,并被识别蛋白所识别。然而,关于 m6A 在透明细胞肾细胞癌(ccRCC)中的作用的研究较少。在这项研究中,我们研究了 TCGA 数据集的 ccRCC 的 RNA-seq 转录组数据,并使用生物信息学分析来检测 m6A RNA 甲基化调节因子与 ccRCC 之间的关系。首先,我们比较了 18 个 m6A RNA 甲基化调节因子在 ccRCC 患者和正常组织中的表达。然后,根据共识聚类将 ccRCC 患者的数据分为两个亚群。使用 LASSO Cox 回归分析构建风险特征以预测 ccRCC 患者的预后。ROC 曲线、单因素 Cox 回归分析和多因素 Cox 回归分析用于验证该风险特征的预测能力。然后,我们通过随机抽样对内进行验证。最后,我们探索了特征基因在一些常见通路中的作用。两个亚群之间的基因分布不同;亚群 2 与性别相关,且预后更差。选择 IGF2BP3、IGF2BP2、HNRNPA2B1 和 METTL14 构建风险特征。高低风险组的总生存率有显著差异(=7.47-12)。ROC 曲线也表明风险特征具有良好的预测意义(AUC=0.72)。这些结果表明,该风险特征可能对 ccRCC 的治疗具有潜在价值。