Systems Metabolomics for HCC Biomarker Discovery

项目总结 代谢组学提供了对生物样本中数千个小分子的全面分析。这是一个 系统生物学的重要组成部分，它将生物体描述为一个完整的、相互作用的网络 不同的生物分子。特别是，复杂疾病的代谢组学特征，如肝细胞 癌症在越来越多的系统生物学方法中扮演着不可或缺的角色。 生物标志物和改进的疾病治疗策略。液-质联用法 液质联用(LC-MS)和气相色谱-质联用(GC-MS)技术已被广泛应用于高纯度有机污染物的分析。 生物样品中数千种代谢物水平的吞吐量比较。然而， 这些平台发现的许多肝细胞癌相关分析物的潜在价值还不够 由于缺乏计算工具和资源，在系统生物学研究中进行了探索：(1)准确 确定大多数分析物的同一性；(2)调查 代谢物在疾病进展中的作用，以及(3)整合多组数据以评估两者之间的关系 在系统层面上疾病和代谢产物之间的关系。部分由于这些限制，重现性较差 已经观察到以前确定的肝细胞癌的代谢产物生物标记物候选，特别是当它们是 通过独立的平台和验证集进行评估。因此，人们寻求新的方法来寻找更多 肝细胞癌的有效生物标志物。该项目旨在通过基于网络的 用于以下方面的方法：(1)确定推定ID的优先顺序，以帮助识别代谢物；(2)进行区分 分析以揭示肝癌和代谢物之间的关系；以及(3)将代谢组数据与 转录学、蛋白质组学和糖学数据，以确定高度有希望的代谢物作为生物标记物候选。 这些候选药物的临床相关性将通过选择性反应监测使用同位素稀释法进行评估。 (SRM)和选择离子监测(SIM)从多个地点招募的独立受试者收集的血清中。 此外，候选者在检测肝癌方面的表现将与已建立的血液生物标记物进行比较。 如甲胎蛋白(AFP)、AFP-L3、DES-γ-羧基凝血酶原(DCP)和高尔基蛋白-73(GP73)。 总之，这个项目试图通过利用网络的力量来寻找肝细胞癌的代谢物生物标记物 建模、多组数据集成、有针对性的定量分析和多中心存储库 生物群落。我们坚信，该项目将产生可靠的血清生物标记物，很可能 在未来的大规模生物标志物验证研究中取得成功。

PROJECT SUMMARY Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It is a crucial component of systems biology, which characterizes an organism as an integrated and interactive network of various biomolecules. In particular, metabolomic characterization of complex diseases such as hepatocellular carcinoma (HCC) plays an indispensable role in the growing systems biology approaches to identify reliable biomarkers and improved disease treatment strategies. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high- throughput comparison of the levels of thousands of metabolites among biological samples. However, the potential values of many HCC-associated analytes discovered by these platforms have been inadequately explored in systems biology research due to lack of computational tools and resources to: (1) accurately determine the identity of most of the analytes; (2) investigate the rewiring and conserved interactions among metabolites in the progression of the disease, and (3) integrate multi-omic data to evaluate the relationship between disease and metabolites at the systems level. Partly due to these limitations, poor reproducibility of previously identified metabolite biomarker candidates for HCC has been observed, especially when they are evaluated through independent platforms and validation sets. Therefore, new methods are sought to find more potent biomarkers for HCC. This project aims to address this challenge with the help of network-based approaches for: (1) prioritizing putative IDs to assist in metabolite identification; (2) performing differential analysis to uncover relationships between HCC and metabolites; and (3) integrating metabolomic data with transcriptomic, proteomic, and glycomic data to identify highly promising metabolites as biomarker candidates. The clinical relevance of these candidates will be evaluated using isotope dilution by selected reaction monitoring (SRM) and selected ion monitoring (SIM) in sera collected from independent subjects recruited in multiple sites. Also, the performance of the candidates in detecting HCC will be compared against established blood biomarkers such as alpha-fetoprotein (AFP), AFP-L3, des-gamma-carboxy prothrombin (DCP), and golgi protein-73 (GP73). In summary, this project seeks to find metabolite biomarkers for HCC by capitalizing on the power of network modeling, multi-omic data integration, targeted quantitative analysis, and a multicenter repository of biospecimens. We strongly believe that the project will lead to reliable serological biomarkers that are likely to succeed in future large-scale biomarker validation studies.