胃癌患者预后相关微RNA预测模型的构建及其应用价值探讨  

Construction and application value of prognosis-associated miRNA prediction model in gastric cancer patients

作　　者：岳犇 王高明 杨鹿笛 崔然[1] 郁丰荣[1] YUE Ben;WANG Gao-ming;YANG Lu-di;CUI Ran;YU Feng-rong(Department of Gastrointestinal Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine)

机构地区：[1]上海交通大学医学院附属仁济医院胃肠外科,上海200127

出　　处：《上海交通大学学报（医学版）》2021年第11期1436-1445,共10页Journal of Shanghai Jiao tong University:Medical Science

基　　金：上海市核酸化学与纳米医学重点实验室“临床+”卓越项目(2020ZYA008)。

摘　　要：目的·通过生物信息学方法建立胃癌患者预后相关微RNA(microRNA,miRNA)的预测模型并探讨其应用价值。方法·收集癌症和肿瘤基因图谱计划(TCGA)数据库中397例胃癌患者的临床病理资料,其中356例临床病理资料完整。397例患者中,男258例,女139例;中位年龄为67岁。将397例患者采用随机抽样法,按7∶3比例分为训练集278例和测试集119例。另从基因表达数据库(Gene Expression Omnibus,GEO)中下载包含20对胃癌及对应癌旁正常组织的miRNA测序数据集GSE93415,从癌组织和癌旁正常组织差异表达的miRNA中筛选出候选差异表达miRNA。基于训练集患者的信息,利用LASSO回归分析,将候选差异表达miRNA拟合成一个可以预测胃癌患者生存率的预后相关miRNA模型。对构建的预后相关miRNA模型的预测效能,分别在训练集和测试集中进行验证,以Log-Rank检验进行生存分析来验证模型的可靠性;以受试者操作特征曲线下面积(area under curve,AUC)分析该模型的预测效能。使用基因表达数据和pRRophetic包中的算法预测患者对化学治疗药物的敏感性。使用Calibration曲线验证恩诺图的准确性。使用一致性指数(consistency index,C-index)检测恩诺图和其他因素建模的一致性。使用决策曲线分析(decision curve analysis,DCA)推测候选因素对于临床决策的帮助。结果·(1)训练集与测试集患者的临床资料和总体生存率比较,差异均无统计学意义(均P>0.05)。(2)差异表达miRNA筛选结果:从GSE93415测序数据集中计算得到111个候选差异表达miRNA,其中20个在癌组织中上调,91个在癌组织中下调。对111个候选差异表达miRNA进行过滤后,得到59个候选差异表达miRNA。(3)构建预后相关miRNA模型:从59个候选miRNA中,筛选出5个与生存相关的miRNA,分别为let-7i-5p、let-7f-5p、miR-708-5p、miR-135b-5p、miR-100-5p,差异表达模式(癌组织对比癌旁组织)均为降低,差异表达倍数分别2.55、2Objective·To construct a prognosis-associated microRNA(miRNA) prediction model in gastric cancer patients based on bioinformatics analysis and evaluate its application value. Methods·The clinicopathological data of gastric cancer patients were downloaded from the Cancer Genome Atlas(TCGA). There were 258 males and 139 females with a median age of 67 years. Three hundred and fifty-six of the 397 patients had complete clinicopathological data. The 397 patients were allocated into training cohort consisting of 278 patients and validation cohort consisting of 119 patients using the random sampling method, with a ratio of 7∶3. A miRNA sequencing dataset GSE93415 containing 20 pairs of gastric cancer and corresponding adjacent normal tissue was downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in gastric cancer and adjacent tissue. A prognosis associated miRNA prediction model was constructed upon survival-associated miRNAs which were selected from candidate miRNAs through LASSO-Cox regression analysis. The performance of prognosis-associated miRNA prediction model was validated in the training cohort and validation cohort. The reliability of the model was evaluated by using Log-Rank test, and the accuracy of the model was evaluated by using the area under curve(AUC) of the receiver operating characteristic curves. Gene expression profiles and algorithms in the pRRophetic package were utilized to predict patients’ sensitivity to chemotherapy drugs. Calibration curves were used to verify the accuracy of the nomogram.Consistency index(C-index) was used to check the consistency of the nomogram and other factors. Decision curve analysis(DCA) was employed to predict the contribution of candidate factors to clinical decision making. Results·(1) There was no significant difference in the baseline and overall survival between the training cohort and validation cohort(P>0.05).(2) There were 111 differentially expressed miRNAs calculated from GSE93415

关 键 词：胃癌 LASSO回归模型 微RNA 预测模型 受试者操作特征曲线 

分 类 号：R735.2[医药卫生—肿瘤]