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山东大学学报 (医学版) ›› 2020, Vol. 1 ›› Issue (7): 47-52.doi: 10.6040/j.issn.1671-7554.0.2020.0663

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胃癌miRNAs预后风险评分模型的构建与应用

史爽1,2,李娟1,2,米琦1,2,王允山1,2,杜鲁涛1,2,王传新1,2   

  1. 1.山东大学第二医院检验医学中心, 山东 济南 250033;2. 山东省肿瘤标志物检测工程实验室, 山东 济南 250033
  • 出版日期:2020-07-20 发布日期:2020-07-10
  • 通讯作者: 王传新. E-mail:cxwang@sdu.edu.cn
  • 基金资助:
    山东省重大科技创新工程项目(2018YFJH0505);山东大学基本科研业务费专项资金资助(2018JC002)

Construction and application of a miRNAs prognostic risk assessment model of gastric cancer

SHI Shuang1,2, LI Juan1,2, MI Qi1,2, WANG Yunshan1,2, DU Lutao1,2, WANG Chuanxin1,2   

  1. 1. Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Shandong, China;
    2. Tumor Marker Detection Engineering Laboratory of Shandong Province, Jinan 250033, Shandong, China
  • Online:2020-07-20 Published:2020-07-10

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摘要： 目的 筛选与胃癌预后存在关联性的微小RNAs(miRNAs)生物标志物,构建风险评分模型用于患者预后评估。 方法 基于人类癌症和肿瘤基因图谱(TCGA)数据库下载胃癌miRNAs表达谱数据及样本相关临床信息,通过“DESeq2”软件包对miRNAs表达谱进行差异分析。采用单因素Cox回归分析和Kaplan-Meier生存分析筛选与预后存在关联性的miRNAs,并将预后miRNAs纳入多因素Cox回归分析用于预后风险评分模型的构建。通过“timeROC”软件包绘制受试者工作特征曲线(ROC),对模型效能进行评价。最后通过在线数据库对miRNAs可能结合的信使RNAs(mRNAs)进行预测,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)预测其功能。 结果 以log2 | Fold Change |>1,P<0.05为标准,筛选得到248个胃癌组织中差异表达的miRNAs。通过单因素Cox回归分析及Kaplan-Meier生存分析筛选到6个与患者总体生存率有关联性的差异表达的miRNAs,随后使用多因素Cox回归分析成功构建胃癌miRNAs预后风险评分模型,风险评分=0.048 35×miR-181b-1 +0.112 06×miR-548d-1+0.068 00×miR-675+0.075 87×miR-708+1.175 21×miR-4640+0.089 89×miR-4709。Kaplan-Meier生存曲线结果显示,风险评分高的患者预后较差(P<0.001);模型5年总体生存率ROC曲线下面积(AUC)为0.776,证明该模型能够有效预测胃癌患者预后风险。GO和KEGG功能分析结果显示,模型miRNAs分子参与多个肿瘤相关代谢通路。 结论 成功构建了miRNAs预后风险评分模型,且该模型对胃癌患者生存状态具有良好的预测效能。

关键词: 胃癌, miRNAs, 预后, TCGA数据库, Cox风险评分模型

Abstract: Objective To identify microRNAs(miRNAs)biomarkers related to the prognosis of gastric cancer patients, and construct a miRNA risk assessment model for survival prediction. Methods The miRNA expression profile of gastric cancer patients and relevant clinical data were obtained from the Cancer Genome Atlas(TCGA)database. Differentially expressed miRNAs were identified with “DESeq2” package. The miRNAs related to prognosis were screened with univariate Cox regression and Kaplan-Meier analysis, which were analyzed with multivariate Cox regression to construct a prognostic risk assessment model. A receiver operating characteristic(ROC)curve was drawn with “time ROC” package to evaluate the effectiveness of the model. Finally, the messenger RNAs(mRNAs)that miRNAs might bind to were predicted with online database, and the possible functions were predicted with gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG). Results With Log2|Fold Change|>1 and P<0.05 as the standards, 248 differentially expressed miRNAs in gastric cancer tissues were identified. Univariate Cox regression and Kaplan-Meier analysis screened out 6 differentially expressed miRNAs which had significant correlation with prognosis to construct a prognostic risk assessment model. The risk score=0.048 35×miR-181b-1 +0.112 06×miR-548d-1+0.068 00×miR-675+0.075 87×miR-708+1.175 21×miR-4640+0.089 89×miR-4709. Kaplan-Meier analysis showed patients with high risks had a poor prognosis(P<0.001). The area under the ROC curve(AUC)of the 5-year overall survival rate was 0.776, indicating the model was able to predict the prognostic risk. GO and KEGG analysis showed miRNAs were involved in a few signaling pathways of gastric cancer. Conclusion A miRNAs prognostic risk assessment model was successfully constructed based on bioinformatics analysis, which was proved by Kaplan-Meier analysis and ROC curve to have good prediction effects on the survival of gastric cancer patients.

Key words: Gastric cancer, miRNAs, Prognosis, TCGA database, Cox hazards regression model

中图分类号: 

  • R574
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