基于TCGA数据库乳头状甲状腺癌miRNA预后风险模型的建立与分析CSCD

目的构建基于微小RNA(miRNA)表达的预测乳头状甲状腺癌(papillary thyroid carcinoma,PTC)患者预后的生存模型。方法从TCGA数据库官方网站上下载PTC miRNA测序数据和患者的临床资料,利用R3.6.0软件中的edgeR包筛选表达失调的miRNA。利用单因素Cox及Lasso回归分析筛选出与患者预后相关的miRNA(P<0.05),进一步使用多因素Cox回归分析建立预后模型的风险评分方程risk score,构建生存预后模型,使用受试者工作特征曲线(ROC)来评价模型的敏感度和特异性。结果与正常甲状腺组织相比,PTC组织中失调表达的miRNA共有75个(|log foldchange|≥2,FDR<0.05),多因素Cox回归分析最终得到基于8个miRNA(hsa-mir-6730、hsa-mir-4709、hsa-mir-196a-2、hsa-mir-146b、hsa-mir-6860、hsa-mir-509-3、hsa-mir-513c、hsa-mir-515-1)的预测患者预后的风险模型。ROC曲线下面积(AUC)分析显示,该模型具有较好的敏感度和特异性(AUC>0.8)。结论成功构建了基于miRNA表达的风险预测模型,该模型可有效预测PTC患者的预后。

Establishment and Analysis of MicroRNA Prognostic Risk Model of Papillary Thyroid Carcinoma Based on TCGA Database

Objective To construct a risk model for predicting the prognosis of papillary thyroid carcinoma (PTC) patients based on the expression of microRNAs (miRNA). Methods The original microRNAs sequencing data and corresponding clinical data of PTC patients were downloaded from the official website of TCGA database. The aberrant expression of microRNAs in PTC was screened by edgeR package in R 3.6.0 software. Univariate and multivariate Cox regression analyses as well as the Lasso regression analysis were utilized to screen out the microRNAs which were related to the prognosis of patients. Then, the risk score equation was established by multivariate Cox regression analysis and the risk prognosis model was constructed. The receiver operating characteristic (ROC) curve was utilized to evaluate the sensitivity and specificity of the model. Results Compared with normal tissues, 75 microRNAs (|log foldchange| ≥2, FDR<0.05) were dysregulated in PTC tissues. Multivariate Cox regression analysis revealed eight microRNAs (hsa-mir-6730, hsa-mir-4709, hsa-mir-196a-2, hsa-mir-146b, hsa-mir-6860, hsa-mir-509-3, hsamir-513c, hsa-mir-515-1) which could be used to construct risk model for predicting the prognosis of patients. The model had good sensitivity and specificity (AUC>0.8). Conclusion A risk prediction model based on the expression of microRNAs has been successfully constructed, and the model could effectively predict the prognosis of thyroid cancer patients.