Clinical prognosis of m6A RNA methylation-regulated genes in thyroid cancer
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摘要:
目的探索m6A RNA甲基化调节基因对THCA的与后续影响。 方法分析癌症基因组图谱（TCGA）数据库中的RNA-seq数据及相关临床信息。通过wilcoxon检验分析肿瘤与正常样品之间m6A RNA甲基化调节基因的不同表达，使用lasso Cox回归分析构建m6A RNA甲基化调节基因与THCA的风险预后模型，使用中位风险评分将THCA患者分层为高风险组和低风险组，kaplan-Meier进行生存分析，使用ROC曲线用来评估预测模型的敏感性和特异性。 结果肿瘤与正常样品之间m6A RNA甲基化调节基因的不同表达，3个基因上调，14个基因下调。lasso Cox回归分析筛选出4个m6A RNA甲基化调节基因IGF2BP2、FTO、IGF2BP1、RBM15。高风险组与低风险组相比，OS明显较低（P=4.278e-02）。ROC曲线中AUC值为0.783，结果显示预测模型较有效。单因素和多因素Cox回归进行模型效能检验，单因素Cox回归分析中HR值为2.083（P < 0.001），多因素Cox回归分析中HR值为2.653（P < 0.001）。 结论m6A RNA甲基化调节基因IGF2BP2、FTO、IGF2BP1、RBM15可以构建预测模型来预测不同的临床亚群患者的OS。 -
关键词:
- m6A RNA甲基化 /
- 甲状腺癌 /
- 临床预后
Abstract:ObjectiveTo explore the correlation of m6A RNA methylation regulators and THCA. MethodsWe analyzed RNAseq data and related clinical information in the Cancer Genome Atlas (TCGA) database. The Wilcoxon test was used to analyze the different expressions of m6A RNA methylation-regulated genes between tumors and normal samples. A lasso Cox regression analysis was used to construct a risk prognosis model of m6A RNA methylation-regulated genes and THCA. The median risk score was used to divide THCA patients into high-risk group and low-risk group. Kaplan-Meier was performed for survival analysis. The ROC curve wa used to assess the sensitivity and feasibility of the prediction model. ResultsDifferent expression of m6A RNA methylation-regulated genes between tumors and normal samples showed 3 up-regulated genes and 14 down-regulated genes. Lasso Cox regression analysis screened four m6A RNA methylation regulating genes IGF2BP2, FTO, IGF2BP1 and RBM15. OS in the high-risk group was significantly lower than that in the low-risk group (P= 4.278e-02). The AUC value in the ROC curve was 0.783. The univariate and multivariate Cox regression were used to test the effectiveness of the model. The HR value in the univariate Cox regression analysis was 2.083, (P < 0.001). The HR value in the multivariate Cox regression analysis was 2.653 (P < 0.001). ConclusionThe m6A RNA methylation regulatory genes IGF2BP2, FTO, IGF2BP1 and RBM15 can construct predictive models to predict OS in patients with different clinical subgroups. -
Key words:
- m6A RNA methylation /
- thyroid cancer /
- clinical prognosis
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图 1 m6A RNA甲基化调节基因在THCA中的表达
Figure 1. The expression of m6A RNA methylation regulators in THCA The heat map (A) and The violin plot (B) of 20 m6A RNA methylation regulators between THCA tumor samples and normal samples. *P < 0.05, **P < 0.01, ***P < 0.001.
图 2 THCA中20个m6A RNA甲基化调节基因的相关性
Figure 2. The correlation between 20 m6A RNA methylation regulators in THCA.
图 3 THCA中20个m6A RNA甲基化调节基因的风险特征
Figure 3. The risk signature of 20 m6A RNA methylation regulators in THCA. A, B: The coefficients calculated by Lasso Cox regression analysis; C: Kaplan-Meier overall survival curve for THCA patients between high risk and low risk group; D: ROC curve showed the predictive value of risk signature. E, G, I: The IHC expression of IGF2BP1、IGF2BP2、RBM15 in normal tissue; F, H, J: The IHC expression of IGF2BP1、IGF2BP2、RBM15 in tumor tissue.
图 4 风险评分和临床特征对THCA患者预后的影响
Figure 4. Effects of the risk signature and clinical information on the prognosis of CESC patients. A: The heat map showed the different expression of m6A RNA methylation regulators and the distribution of clinical information between high risk group and low risk group; Univariate Cox regression (B) and Multivariate Cox regression (C) analysis of the correlation between clinical information and overall survival.
图 5 riskScore在THCA患者临床亚组中的预测价值
Figure 5. Prognostic value of the risk signature in THCA patients stratified into different clinical subgroup. Kaplan-Meier survival for patient in (A) N0 and (B) stage 3/4
图 6 在验证集中验证预后模型
Figure 6. Validation of the prognostic signature in tested group. A: Kaplan-Meier overall survival curve for THCA patients between high risk and low risk group in tested group; B: ROC curve showed the predictive value of risk signature in tested group; C: Univariate Cox regression analysis the correlation between clinical information and overall survival in tested group. *P < 0.05.
表 1 风险分组临床特征(n, %)
Table 1. The clinical information in different risk group
Characterisitic High risk Low risk Age (years) ≤60 186 (75) 200 (80.32) >60 62 (25) 49 (19.68) Gender Male 64 (25.8) 70 (28.11) Female 184 (74.2) 179 (71.89) Stage Ⅰ 147 (59.27) 132 (53.01) Ⅱ 30 (12.10) 22 (8.83) Ⅲ 54 (21.77) 58 (23.29) Ⅳ 17 (6.86) 37 (14.87) 
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