Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Department of Organ Transplantation, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China.
Sci Rep. 2023 Aug 4;13(1):12645. doi: 10.1038/s41598-023-39935-4.
In recent years, RNA methylation modification has been found to be related to a variety of tumor mechanisms, such as rectal cancer. Clear cell renal cell carcinoma (ccRCC) is most common in renal cell carcinoma. In this study, we get the RNA profiles of ccRCC patients from ArrayExpress and TCGA databases. The prognosis model of ccRCC was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis, and the samples were stratified into low-high risk groups. In addition, our prognostic model was validated through the receiver operating characteristic curve (ROC). "pRRophetic" package screened five potential small molecule drugs. Protein interaction networks explore tumor driving factors and drug targeting factors. Finally, polymerase chain reaction (PCR) was used to verify the expression of the model in the ccRCC cell line. The mRNA matrix in ArrayExpress and TCGA databases was used to establish a prognostic model for ccRCC through LASSO regression analysis. Kaplan Meier analysis showed that the overall survival rate (OS) of the high-risk group was poor. ROC verifies the reliability of our model. Functional enrichment analysis showed that there was a obviously difference in immune status between the high-low risk groups. "pRRophetic" package screened five potential small molecule drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706). Protein interaction network shows that epidermal growth factor receptor [EGRF] and estrogen receptor 1 [ESR1] are tumor drivers and drug targeting factors. To further analyze the differential expression and pathway correlation of the prognosis risk model species. Finally, polymerase chain reaction (PCR) showed the expression of YTHN6-Methyladenosine RNA Binding Protein 1[YTHDF1], TRNA Methyltransferase 61B [TRMT61B], TRNA Methyltransferase 10C [TRMT10C] and AlkB Homolog 1[ALKBH1] in ccRCC cell lines. To sum up, the prognosis risk model we created not only has good predictive value, but also can provide guidance for accurately predicting the prognosis of ccRCC.
近年来，RNA 甲基化修饰被发现与多种肿瘤机制有关，例如直肠癌。透明细胞肾细胞癌（ccRCC）是肾细胞癌中最常见的一种。在本研究中，我们从 ArrayExpress 和 TCGA 数据库中获取了 ccRCC 患者的 RNA 图谱。通过最小绝对收缩和选择算子（LASSO）回归分析，构建了 ccRCC 的预后模型，并将样本分层为低风险组和高风险组。此外，我们的预后模型通过接收者操作特征曲线（ROC）进行了验证。“pRRophetic”软件包筛选出了五种潜在的小分子药物。蛋白质相互作用网络探索了肿瘤驱动因素和药物靶点因素。最后，聚合酶链反应（PCR）验证了模型在 ccRCC 细胞系中的表达。通过 LASSO 回归分析，利用 ArrayExpress 和 TCGA 数据库的 mRNA 矩阵建立了 ccRCC 的预后模型。Kaplan-Meier 分析表明，高风险组的总生存率（OS）较差。ROC 验证了我们模型的可靠性。功能富集分析表明，高低风险组之间的免疫状态存在明显差异。“pRRophetic”软件包筛选出了五种潜在的小分子药物（A.443654、A.770041、ABT.888、AG.014699、AMG.706）。蛋白质相互作用网络表明，表皮生长因子受体[EGRF]和雌激素受体 1[ESR1]是肿瘤驱动因素和药物靶点因素。进一步分析预后风险模型物种的差异表达和通路相关性。最后，聚合酶链反应（PCR）显示了 YTHN6-甲基腺苷 RNA 结合蛋白 1[YTHDF1]、tRNA 甲基转移酶 61B[TRMT61B]、tRNA 甲基转移酶 10C[TRMT10C]和 AlkB 同源物 1[ALKBH1]在 ccRCC 细胞系中的表达。总之，我们构建的预后风险模型不仅具有良好的预测价值，而且还可以为准确预测 ccRCC 的预后提供指导。
