基于GWAS的肺癌预后相关miRNA协同调控网络的识别及其调控机制研究

项目名称： 基于GWAS的肺癌预后相关miRNA协同调控网络的识别及其调控机制研究

项目编号： No.81502878

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 医药、卫生

项目作者： 李雪莲

作者单位： 中国医科大学

项目金额： 18万元

中文摘要： 肺癌死亡率位于恶性肿瘤死因首位。研究预后相关遗传因素，筛选有效分子标志物是开展肺癌预后预测和靶向治疗的必要手段。miRNA通过协同作用参与肿瘤发生发展的生物过程，是潜在的预后分子标志物。本研究拟整合GWAS和表达谱数据优化筛选预后相关miRNA，分析miRNA SNPs对肺癌预后的影响及miRNA协同调控作用。首先利用基因集分析GWAS数据中miRNA SNPs与肺癌预后相关性，利用表达谱分析miRNA-mRNA失调控关系，建立miRNA分类器优化筛选预后相关miRNA；进一步利用主成分分析等方法建立miRNA-GRS肺癌预后模型，评价并验证其预测效果；进而利用miRNA共表达和靶基因调控关系识别肺癌预后相关miRNA协同调控网络，利用相关实验探讨miRNA协同调控网络对靶基因表达、细胞凋亡侵袭等能力的影响及其机制。研究结果将为肺癌的预后诊断和肺癌靶向性治疗提供理论和实验依据。

中文关键词： 肺癌；预后；miRNA协同调控网络；全基因组关联研究；遗传风险分数

英文摘要： Lung cancer is by far the leading cause of cancer-related mortality in most populations worldwide. Effective molecular markers are the keys to develop targeted therapy and prognostic prediction for lung cancer. miRNAs, which synergistically target to mRNA sequences, are associated with tumor biology. In this study we analyze miRNA synergistic network and the relationship between lung cancer survival and miRNA SNPs based on GWAS and expression profile data. First, gene sets are used to identify prognosis related miRNA SNPs in GWAS data, then, miRNAs and mRNA expression profiles in lung cancer tissues are used to identify survival associated miRNA target–dysregulated network and construct the support vector machine classifier. The SVM classifier is applied to prioritize lung cancer prognosis related miRNAs. After that, a genetic risk score (GRS) of miRNA SNPs is conducted by the methods of principal component analysis and random survival forests. Then we demonstrate the utility of miRNA-GRS to predict survival time in another lung cancer population. miRNA synergistic network are identified by integrating analysis of the co-expression relationship and the predicted targets information. Flow cytometry is used to detect the cell apoptosis and cell cycle, and real-time PCR are employed to measure expressive levels of targets after up-regulation of miRNAs using miRNA mimics together or respectively. The pmirGLO Dual-Luciferase Vector contains 3′UTR cloned of predicted targets are used to analyze miRNA co-targets. Results of this study will provide theoretical basis and practical guidance for prognostic prediction and targeted therapy of lung cancer.

英文关键词： lung cancer;prognosis;miRNA functional synergistic network;GWAS;genetic risk score