整合人类代谢网络和基因组信息的缺血性心肌病生物标志物识别及机制研究

The effective identification of biomarkers for ischemic cardiomyopathy (ICM), taking fully account of the interaction between genetic and metabolic factors in its process, is of great significance for the elucidation of ICM molecular mechanism. Based on previous researches of our research group, an innovative idea was proposed in this project. We aimed to identify ICM biomarkers and reveal the pathogenesis by integrating human metabolic networks and genomic information out of the theory of systems biology and methods of bioinformatics. By investigating the genome-wide sequencing data of ICM and normal samples, disease risk values of mRNA, miRNAs and lncRNA were evaluated. Their expression similarities were assessed. Regulatory relationships that were significantly changed in ICM status were screened out. The above information was further integrated into the global human metabolic network to construct the ICM related heterogeneous network. A novel random walk-based biomarker identification algorithm was developed to identify ICM biomarkers, i.e. ICM related mRNAs, miRNAs and lncRNAs. Effectiveness of the algorithm was also evaluated. The ICM pathogenesis analysis platform was built after the screening of significant ICM-related risk metabolic pathways. We hope the study of this project could not only contribute to add missing relevant information to current public databases, but also provide a new perspective for the pathogenesis elucidation of other metabolism-related complex diseases.

充分考虑遗传和代谢因素在缺血性心肌病（ICM）发病过程中的交互作用，有效识别ICM生物标志物，对阐明ICM的分子机制具有重要意义。本项目在前期研究的基础上，提出了基于系统生物学理论和生物信息学方法，整合人类代谢网络和基因组信息，实现ICM生物标志物识别和发病机制揭示的创新思路。通过分析ICM和正常样本的全基因组测序数据，评估mRNA、miRNA和lncRNA的疾病风险，度量其表达相似性，筛选出ICM状态下显著改变的调控关系；将以上信息整合到全局人类代谢网络，构建出ICM相关异质网络；开发新的建立于随机行走算法基础上的生物标志物识别算法，识别出ICM相关的mRNA、miRNA和lncRNA等生物标志物并评估算法的有效性；通过风险代谢通路筛选，构建出ICM发病机制分析平台。期望本项目的研究不仅有助于补充当前公共数据库中缺少的相关信息，还为其他代谢相关复杂疾病发病机制的阐明提供新的视角。

缺血性心肌病（ICM）是一种常见的致人死亡的心脏疾病，而现有数据库中未存储ICM的有效生物标志物，不利于对该疾病机制的深入研究及诊断和治疗的需要。本项目致力于从系统生物学的角度，利用生物信息学方法，整合基因组和代谢等多组学信息，针对ICM生物标志物识别问题开展研究。首先从ICM高通量测序数据获得mRNA、miRNA和lncRNA间的调控关系，开发差异共表达分析方法，构建了显著差异表达内源竞争RNA三元组，并基于功能信息，识别了能够有效区分ICM患者和正常样本的mRNA和lncRNA作为生物标志物，根据以上结果构建了ICM生物标志物平台。进一步整合心肌病表达、代谢或蛋白质互作数据，建立心肌病相关网络，开发了基于网络的新的生物标志物识别算法，有效挖掘出心肌病的生物标志物及风险模块，并进行了功能分析、文献证实及其他数据的验证。此外，本项目还对心血管疾病、乳腺癌等其他慢性复杂疾病进行了扩展性研究，基于疾病风险功能单核苷酸多态性（SNP）和疾病相关网络，利用生物信息学相关方法设计新算法，成功地识别出复杂疾病的易感基因和风险模块，为这些疾病分子机制的探究提供了新的视角。.三年来，在本项目基金的支持下，本项目按照研究计划，针对关键学术问题，围绕整体研究目标，顺利开展，取得了较好的研究成果。共发表论文9篇，其中SCI收录论文8篇，核心期刊论文1篇；培养硕士研究生4人。