Zhang Zhen, Cao Chunhua, Zhou Chun-Li, Li Xilong, Miao Changhong, Shen Li, Singla Rajeev K, Lu Xihua
Department of Anesthesiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
Department of Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China; Institute of Oncology, Hubei University of Arts and Science, Xiangyang 441021, China.
Transl Oncol. 2023 Oct;36:101741. doi: 10.1016/j.tranon.2023.101741. Epub 2023 Jul 29.
Many studies have demonstrated the crucial roles of 5-methylcytosine (m5C) RNA methylation in cancer pathogenesis.
Two datasets, including TCGA-KIRP and ICGC, and related clinical information were downloaded, where the expression of 13 m5C regulators was examined. We applied LASSO regression to construct a multi-m5C-regulator-based signature in the TCGA cohort, which was further validated using the ICGC cohort. Univariate and multivariate Cox regressions were applied to evaluate the independent prognostic value of our model. The differences in biological functions and immune characterizations between high and low-risk groups divided based on the risk scores were also investigated via multiple approaches, such as enrichment analyses, mutation mining, and immune scoring. Finally, the sensitivities of commonly used targeted drugs were tested, and the connectivity MAP (cMAP) was utilized to screen potentially effective molecules for patients in the high-risk group. Experimental validation was done following qPCR tests in Caki-2 and HK-2 cell lines.
3 m5C regulators, including ALYREF, DNMT3B and YBX1, were involved in our model. Survival analysis revealed a worse prognosis for patients in the high-risk group. Cox regression results indicated our model's superior predictive performance compared to single-factor prognostic evaluation. Functional enrichment analyses indicated a higher mutation frequency and poorer tumor microenvironment of patients in the high-risk group. qPCR-based results revealed that ALYREF, DNMT3B, and YBX1 were significantly up-regulated in Caki-2 cell lines compared with HK-2 cell lines. Molecules like BRD-K72451865, Levosimendan, and BRD-K03515135 were advised by cMAP for patients in the high-risk group.
Our study presented a novel predictive model for KIRP prognosis. Furthermore, the results of our analysis provide new insights for investigating m5C events in KIRP pathogenesis.
许多研究已证明5-甲基胞嘧啶（m5C）RNA甲基化在癌症发病机制中的关键作用。
下载了包括TCGA-KIRP和ICGC在内的两个数据集以及相关临床信息，检测了13种m5C调节因子的表达。我们应用LASSO回归在TCGA队列中构建基于多m5C调节因子的特征，并用ICGC队列进一步验证。应用单因素和多因素Cox回归评估我们模型的独立预后价值。还通过多种方法，如富集分析、突变挖掘和免疫评分，研究了基于风险评分划分的高风险组和低风险组之间生物学功能和免疫特征的差异。最后，测试了常用靶向药物的敏感性，并利用连接性图谱（cMAP）为高风险组患者筛选潜在有效分子。在Caki-2和HK-2细胞系中进行qPCR测试后进行实验验证。
我们的模型涉及3种m5C调节因子，包括ALYREF、DNMT3B和YBX1。生存分析显示高风险组患者预后较差。Cox回归结果表明，与单因素预后评估相比，我们的模型具有更好的预测性能。功能富集分析表明，高风险组患者的突变频率更高，肿瘤微环境更差。基于qPCR的结果显示，与HK-2细胞系相比，Caki-2细胞系中ALYREF、DNMT3B和YBX1显著上调。cMAP为高风险组患者推荐了如BRD-K72451865、左西孟旦和BRD-K03515135等分子。
我们的研究提出了一种新的KIRP预后预测模型。此外，我们的分析结果为研究KIRP发病机制中的m5C事件提供了新的见解。
