Systems Metabolomics for HCC Biomarker Discovery

HCC 生物标志物发现的系统代谢组学

• 批准号: 9894874
• 负责人: Habtom W Ressom
• 金额: $ 20万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894874
• 关键词: Address; Age; Alcohol consumption; Algorithms; Biochemical Pathway; Biological; Biological Markers; Blood; Chemicals; Cirrhosis; Collection; Complex; Coupled; Data; Databases; Development; Disease; Disease Progression; Egypt; Evaluation; Future; Gas Chromatography; Gender; Gene Proteins; Goals; Golgi Apparatus; Ions; Isotopes; Knowledge; Liquid Chromatography; Liver; Liver Cirrhosis; Mass Fragmentography; Mass Spectrum Analysis; Measurement; Methods; Monitor; Multiomic Data; Multivariate Analysis; Network-based; Organism; Patients; Performance; Play; Polysaccharides; Primary carcinoma of the liver cells; Proteins; Proteomics; Reaction; Reporting; Reproducibility; Research; Research Personnel; Risk Factors; Role; Sampling; Sensitivity and Specificity; Serological; Serum; Site; Statistical Data Interpretation; Statistical Methods; Structure; System; Systems Biology; Tissues; Universities; Validation; Virus Diseases; acarboxy prothrombin; alpha-Fetoproteins; base; biomarker discovery; biomarker validation; candidate marker; clinically relevant; cohort; computerized tools; computing resources; data integration; disease heterogeneity; improved; innovation; liquid chromatography mass spectrometry; metabolomics; network models; platform-independent; prevent; recruit; repository; selected ion monitoring; small molecule; transcriptomics; treatment strategy; validation studies

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.

项目摘要 代谢组学提供了对生物样品中数千种小分子的全面分析。这是一个 系统生物学的一个重要组成部分，它将有机体描述为一个集成的和相互作用的网络 不同的生物分子。特别是，复杂疾病如肝细胞癌的代谢组学表征 肝癌（HCC）在不断发展的系统生物学方法中起着不可或缺的作用， 生物标志物和改进的疾病治疗策略。液相色谱-质谱联用 液相色谱-质谱联用技术（LC-MS）和气相色谱-质谱联用技术（GC-MS）已被广泛用于高通量分析。 生物样品中数千种代谢物水平的通量比较。但 这些平台发现的许多HCC相关分析物的潜在价值还不充分， 由于缺乏计算工具和资源，系统生物学研究中的探索：（1）准确 确定大多数分析物的身份;（2）研究重新布线和保守的相互作用， 代谢物在疾病进展中的作用，以及（3）整合多组学数据以评估 疾病和代谢物之间的联系部分由于这些限制， 已经观察到先前鉴定的HCC的代谢物生物标志物候选物，特别是当它们是 通过独立平台和验证集进行评估。因此，寻求新的方法来发现更多的 HCC的有效生物标志物。该项目旨在通过基于网络的 方法：（1）优先考虑推定的ID，以帮助代谢物鉴定;（2）进行差异化 分析以揭示HCC和代谢物之间的关系;以及（3）将代谢组学数据与 转录组学、蛋白质组学和糖组学数据来鉴定高度有希望的代谢物作为生物标志物候选物。 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进行评价 (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，候选人在检测HCC方面的表现将与已建立的血液生物标志物进行比较 如甲胎蛋白（AFP）、AFP-L3、脱-γ-羧基凝血酶原（DCP）和高尔基体蛋白-73（GP 73）。 总之，本项目旨在通过利用网络的力量来寻找HCC的代谢物生物标志物 建模，多组学数据集成，有针对性的定量分析，以及多中心的 生物标本我们坚信，该项目将导致可靠的血清学生物标志物， 在未来的大规模生物标志物验证研究中取得成功。