快速确定心血管疾病相关lncRNA的生物信息学方法和技术研究

Cardiovascular disease is one leading cause of human death. Therefore, it is emergently important to find novel biomolecules for the diagnosis and therapy of cardiovascular disease. LncRNAs are a big class of new noncoding RNA molecules. It is known that lncRNAs are closely associated with cardiovascular disease and could have important roles in cardiovascular disease diagnosis and therapy. Currently, more than 90,000 lncRNAs have been identified in the human genome. However, only less than 1% of the total lncRNAs were reported to be associated with cardiovascular disease. And we have no any idea about the relationship between the rest 99% of lncRNAs and cardiovascular disease. Therefore, it is emergently important to develop methods and technology to the rapid dissection of the relationship between lncRNAs and cardiovascular disease. I have a long time experience on the bioinformatics of cardiovascular lncRNAs, presented a number of bioinformatics mehtods and technology, and found several cardiovascular disease associated lncRNAs. Based on the above observation, in this project, I plan to present more bioinformatics methods and technology to answer the following scientific questions: (1) the global and systematic prediction of the relationship between the 90,000 human lncRNAs and various cardiovascular diseases; (2) the global and systematic prediction of the lncRNAs important in cardiovascular disease diagnosis and therapy. Finally, we plan to validate the presented methods by performing biological experiments for the predicted abdominal aortic aneurysm (AAA)-associated lncRNAs.

心血管疾病是主要的致死性疾病，对其诊疗迫切需要更新的生物分子。lncRNA是一大类重要的新型非编码RNA分子，和心血管疾病关系密切，对心血管疾病诊疗具有重要意义。目前人类基因组已发现约9万条lncRNA，然而有实验报道的心血管疾病相关lncRNA不足1%，我们对99%以上的lncRNA在心血管疾病中的意义一无所知。因此急需开发能够快速解答lncRNA和心血管疾病关系的生物信息学方法和技术。申请人在心血管lncRNA生物信息学领域具有多年研究经验，提出和开发了系列方法和技术，发现了多个心血管疾病相关lncRNA。基于前期工作基础，本课题拟继续提出和开发系列生物信息学方法和技术，以期全面、系统的回答以下科学问题：(1)预测9万余条人类lncRNA和各种心血管疾病的一 一对应关系；(2)预测对于心血管疾病诊疗具有重要意义的lncRNA，并选择腹主动脉瘤相关lncRNA进行生物学验证。

在本项目资助下，我们取得以下主要结果和进展。（1）生物信息数据库方面：对LncRNA疾病数据库LncRNADisease做了重大更新，整理了因果性数据；（2）生物信息算法方面：提出lncRNA、miRNA重要性计算方法GIC、MIC和miES等；建立了药物集合分析算法DrugPattern以及性别差异基因、药物预测算法SexBiasedGene；建立了基于矩阵分解的外显子组数据分析算法；提出了单样本miRNA集合分析算法sTAM；（3）转化应用研究方面：发现一个糖脂代谢新lncRNA分子-lncSHGL；发现降压药醋丁洛尔和美托洛尔存在性别差异；发现动静脉血液miRNA存在差异；发现健康个体使用二甲双胍存在促高血压风险。