Wu Jiajin, Hou Chao, Wang Yuhao, Wang Zhongyuan, Li Pu, Wang Zengjun
The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing 210029, China.
Int J Genomics. 2021 Sep 28;2021:3803724. doi: 10.1155/2021/3803724. eCollection 2021.
Recent research found that N5-methylcytosine (mC) was involved in the development and occurrence of numerous cancers. However, the function and mechanism of mC RNA methylation regulators in clear cell renal cell carcinoma (ccRCC) remains undiscovered. This study is aimed at investigating the predictive and clinical value of these mC-related genes in ccRCC.
Based on The Cancer Genome Atlas (TCGA) database, the expression patterns of twelve mC regulators and matched clinicopathological characteristics were downloaded and analyzed. To reveal the relationships between the expression levels of mC-related genes and the prognosis value in ccRCC, consensus clustering analysis was carried out. By univariate Cox analysis and last absolute shrinkage and selection operator (LASSO) Cox regression algorithm, a mC-related risk signature was constructed in the training group and further validated in the testing group and the entire cohort. Then, the predictive ability of survival of this mC-related risk signature was analyzed by Cox regression analysis and nomogram. Functional annotation and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied to further explore the biological function and potential signaling pathways. Furthermore, we performed qRT-PCR experiments and measured global mC RNA methylation level to validate this signature in vitro and tissue samples.
In the TCGA-KIRC cohort, we found significant differences in the expression of mC RNA methylation-related genes between ccRCC tissues and normal kidney tissues. Consensus cluster analysis was conducted to separate patients into two mC RNA methylation subtypes. Significantly better outcomes were observed in ccRCC patients in cluster 1 than in cluster 2. mC RNA methylation-related risk score was calculated to evaluate the prognosis of ccRCC patients by seven screened mC RNA methylation regulators (NOP2, NSUN2, NSUN3, NSUN4, NSUN5, TET2, and DNMT3B) in the training cohort. The AUC for the 1-, 2-, and 3-year survival in the training cohort were 0.792, 0.675, and 0.709, respectively, indicating that the risk signature had an excellent prognosis prediction in ccRCC. Additionally, univariate and multivariate Cox regression analyses revealed that the risk signature could be an independent prognostic factor in ccRCC. The results of ssGSEA suggested that the immune cells with different infiltration degrees between the high-risk and low-risk groups were T cells including follicular helper T cells, Th1_cells, Th2_cells, and CD8+_T_cells, and the main differences in immune-related functions between the two groups were the interferon response and T cell costimulation. In addition, qRT-PCR experiments confirmed our results in renal cell lines and tissue samples.
According to the seven selected regulatory factors of mC RNA methylation, a risk signature associated with mC methylation that can independently predict prognosis in patients with ccRCC was developed and further verified the predictive efficiency.
近期研究发现，N5-甲基胞嘧啶（mC）参与了多种癌症的发生发展过程。然而，mC RNA甲基化调节因子在肾透明细胞癌（ccRCC）中的功能和机制仍未被揭示。本研究旨在探讨这些与mC相关基因在ccRCC中的预测价值和临床意义。
基于癌症基因组图谱（TCGA）数据库，下载并分析了12种mC调节因子的表达模式及匹配的临床病理特征。为揭示mC相关基因表达水平与ccRCC预后价值之间的关系，进行了一致性聚类分析。通过单因素Cox分析和最小绝对收缩和选择算子（LASSO）Cox回归算法，在训练组中构建了一个与mC相关的风险特征，并在测试组和整个队列中进一步验证。然后，通过Cox回归分析和列线图分析该与mC相关的风险特征对生存的预测能力。应用功能注释和单样本基因集富集分析（ssGSEA）进一步探索其生物学功能和潜在信号通路。此外，我们进行了qRT-PCR实验并测量了整体mC RNA甲基化水平，以在体外和组织样本中验证该特征。
在TCGA-KIRC队列中，我们发现ccRCC组织与正常肾组织中mC RNA甲基化相关基因的表达存在显著差异。进行一致性聚类分析将患者分为两种mC RNA甲基化亚型。1组ccRCC患者的预后明显优于2组。通过训练队列中筛选出的7种mC RNA甲基化调节因子（NOP2、NSUN2、NSUN3、NSUN4、NSUN5、TET2和DNMT3B）计算mC RNA甲基化相关风险评分，以评估ccRCC患者的预后。训练队列中1年、2年和3年生存率的AUC分别为0.792、0.675和0.709，表明该风险特征在ccRCC中具有良好的预后预测能力。此外，单因素和多因素Cox回归分析显示，该风险特征可能是ccRCC的独立预后因素。ssGSEA结果表明，高危组和低危组之间不同浸润程度的免疫细胞为T细胞，包括滤泡辅助性T细胞、Th1细胞、Th2细胞和CD8 + T细胞，两组之间免疫相关功能的主要差异在于干扰素反应和T细胞共刺激。此外，qRT-PCR实验在肾癌细胞系和组织样本中证实了我们的结果。
根据7种选定的mC RNA甲基化调节因子，开发了一种与mC甲基化相关的风险特征，可独立预测ccRCC患者的预后，并进一步验证了其预测效率。
