功能代谢组学新方法及其在前列腺癌研究中的应用

代谢组学是研究内源性代谢物变化的科学，已被用于疾病分型和标志物发现等。如何解读和利用代谢组学获得的大数据，并与重要生物功能相关联是该领域研究的重点和难点，也是世界各国竞争的焦点。本项目围绕“与重要生物学事件相关的关键生物分子及其途径挖掘”这一科学目标，拟从非靶向代谢组质谱信号的高效获取与规模化定性、关键生物分子及相关途径锁定和生物功能确证三方面发展功能代谢组学研究的新策略。发展UHPLC-HRMS弱质谱信号识别算法和分子结构关联网络的规模化定性方法，以实现代谢组信息的深度注释。构建多层次分子互作网络，锁定关键生物分子及途径。以前列腺癌（PCa）为例，对关键生物分子及途径进行挖掘，通过生物学验证阐释PCa发生发展的分子机制。本项目的实施可为生物功能研究和确证提供重要指导，为疾病治疗提供靶点，并进一步拓展代谢组学的深度和广度。

Metabolomics is the science of investigating metabolic phenotypes based on the slight variations in endogenous metabolites. It has been a powerful tool for the purpose of disease classification and metabolic biomarker discovery. Recently, how to interpret and utilize big data obtained by metabolomics and define the active metabolites related with important biological functions, has been the focus and puzzle of the research in the field of metabolomics. It has also been the competitive focus all around the world. To this end, the present project aims to develop novel functional metabolomics strategies via focusing on the scientific goal of “mining the key biological molecules and related pathways associated with important biological events”. Our goal will be achieved from three aspects including: i) high-efficiency acquisition and large-scale identification of non-targeted metabolic mass spectrum signal; ii) locking of key biological molecules and related pathways; and iii) confirmation of biological function of key biomolecules. It is anticipated to realize the effective annotation of metabolome information through developing the recognition algorithm of weak mass spectrum signal and the large-scale qualitative method based on Molecular Structure Association Network. Based on this, a multi-level molecular interaction network will be constructed to lock the key biomolecules and related pathways. In the end, prostate cancer (PCa) will be used as an application example. The relevant key biological molecules and related pathways of PCa will be excavated and the molecular mechanism related to the development of PCa will be explained via the biological verification. The implementation of this project can provide important guidance for biological function research and verification, provide a target for disease treatment, and further expand the depth and breadth of metabolomics.