Zhang Jun, Piao Hai-Yan, Wang Yue, Meng Xiang-Yu, Yang Dong, Zhao Yan, Zheng Zhi-Chao
Gastric Cancer Department, Liaoning Province Cancer Hospital & Institute (Cancer Hospital of China Medical University), Shenyang City, Liaoning Province 110042, People's Republic of China.
Medical Oncology Department of Gastrointestinal Cancer, Liaoning Province Cancer Hospital & Institute (Cancer Hospital of China Medical University), Shenyang City, Liaoning Province 110042, People's Republic of China.
Onco Targets Ther. 2020 Oct 23;13:10785-10795. doi: 10.2147/OTT.S276239. eCollection 2020.
Gastric cancer (GC) accounts for high mortality. RNA methylation has recently gained interest as markers in specific tumors. This study aimed to uncover the function of the roles of 25 RNA methylation regulators in GC.
RNA sequence and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. "STRING" and R were performed to analyze the correlation among the methylase. COX and LASSO were performed to screen for prognostic associated RNA methylation regulators. A prognostic model was established based on the expression of methylase. RT-PCR and immunohistochemistry detected the expression of methylase in GC cells and tissue. Kaplan-Meier curve and Cox analysis were applied to evaluate the effectiveness of the model.
The prediction model was established based on the expression of m6A RNA methylation regulators FTO (fat mass and obesity-associated) and RBM15 (RNA binding motif protein 15). Based on the model, GC patients were divided into "high risk" and "low risk" groups to compare the differences in survival. The model was re-evaluated with the clinical data of our center.
The two-methylase combination model was an independent prognostic factor of GC.
胃癌（GC）死亡率高。RNA甲基化最近作为特定肿瘤的标志物受到关注。本研究旨在揭示25种RNA甲基化调节因子在胃癌中的作用。
从癌症基因组图谱（TCGA）数据库下载RNA序列和临床数据。使用“STRING”和R软件分析甲基化酶之间的相关性。进行COX和LASSO分析以筛选与预后相关的RNA甲基化调节因子。基于甲基化酶的表达建立预后模型。通过RT-PCR和免疫组织化学检测GC细胞和组织中甲基化酶的表达。应用Kaplan-Meier曲线和Cox分析评估模型的有效性。
基于m6A RNA甲基化调节因子FTO（脂肪量和肥胖相关）和RBM15（RNA结合基序蛋白15）的表达建立了预测模型。基于该模型，将GC患者分为“高风险”和“低风险”组以比较生存差异。用本中心的临床数据对该模型进行了重新评估。
双甲基化酶联合模型是GC的独立预后因素。
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