Uncovering Roles of Metabolites in Colorectal Cancer Etiology

揭示代谢物在结直肠癌病因学中的作用

• 批准号: 10201868
• 负责人: Xiang Shu
• 金额: $ 24.91万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201868
• 关键词: Affect; Asia; Asians; Award; Biological; Biological Markers; Blood; Blood specimen; Body mass index; Branched-Chain Amino Acids; Cancer Biology; Cancer Etiology; Clinical; Cohort Studies; Collaborations; Colorectal; Colorectal Cancer; Communities; Data; Databases; Development; Diagnosis; Diagnostic; Dietary intake; Disease; Dyslipidemias; Energy Metabolism; Etiology; European; Framingham Heart Study; Genetic; Genetic Predisposition to Disease; Genotype; Goals; Healthy Eating; Homeostasis; Intake; Investigation; K-Series Research Career Programs; Knowledge; Life Style; Link; Liquid Chromatography; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Meat; Mediation; Mediator of activation protein; Mentors; Metabolic; Metabolic Diseases; Metabolism; Modeling; Molecular Epidemiology of Cancer; Molecular Genetics; Nested Case-Control Study; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Observational Study; Parents; Participant; Pathway interactions; Phase; Physical activity; Pilot Projects; Plasma; Play; Population Genetics; Predisposition; Prevention; Prospective Studies; Research; Research Personnel; Risk; Risk Assessment; Risk Factors; Role; Sample Size; Susceptibility Gene; Techniques; Time; Training; Translating; United States; Vegetables; Waist-Hip Ratio; Woman; base; cancer biomarkers; cancer diagnosis; cancer initiation; cancer risk; career; case control; colorectal cancer prevention; colorectal cancer risk; cost; cost efficient; density; design; genetic epidemiology; genetic predictors; genetic variant; genome wide association study; genome-wide; genomic locus; good diet; high throughput technology; improved; in silico; indexing; innovation; instrument; lifestyle factors; male health; meetings; metabolome; metabolomics; novel; novel marker; offspring; physical inactivity; potential biomarker; predictive modeling; prevent; prospective; screening; tandem mass spectrometry; tool; tumor metabolism; tumor progression

PROJECT SUMMARY Metabolic perturbation or reprogramming is considered one of the hallmarks of cancer. Several common metabolic disorders, such as obesity, type 2 diabetes, and dyslipidemia, have been linked to colorectal cancer (CRC) risk. With the advances in the techniques of metabolomics, hundreds to thousands of metabolites can be systematically measured in an agnostic manner. For a well-designed prospective metabolomic study of cancer, the identification of novel risk-associated biomarkers will shed new lights on the cancer etiology and related biological pathways. Conventional studies evaluating metabolites are costly and may suffer from biases commonly encountered in the observational studies. It has been increasingly recognized that genetic factors play a significant role in determining the levels of many metabolites. Herein, I propose a cost-efficient approach to systematically evaluate the associations between plasma levels of metabolites and CRC risk. I also incorporate didactic training on molecular/genetic epidemiology, causal inference, cancer biology and metabolism, and population genetics in this application to accomplish my research and career goals described below. The proposed research is composed of four aims. In aim 1, I will build models to predict metabolite levels using existing metabolomics and high-density genotyping data from a public database and possible data generated at Vanderbilt. In aim 2, metabolites with satisfactory prediction accuracy and meeting other criteria will be evaluated in three large-scale CRC consortia with a combined sample size of ~80,900 cases and 115,000 controls to assess the associations of genetically predicted metabolite levels with CRC risk. In collaboration with other investigators during the R00 phase of this award, I will then conduct a nested case- control study to confirm up to ten metabolites identified from in silico analysis by direct measurement of these metabolites using pre-diagnosis plasma collected in three large cohort studies (aim 3). Finally, I will evaluate the potential mediation effects of metabolites for the associations between lifestyle factors including obesity (body mass index and waist-hip-ratio), physical activity, red meat intakes, vegetable intakes, healthy eating index and CRC risk (aim 4). This innovative study will, for the first time, systematically search for novel metabolite biomarkers for CRC risk using genetic instruments and validate the identified associations by direct measurement. In addition, this study will expand the understanding of underlying mechanisms causing CRC. The proposed career development award will help me building advanced knowledge of genetic and molecular epidemiology, cancer metabolism, and CRC etiology and risk assessment to transition to a successful independent investigator.

项目摘要 代谢扰动或重编程被认为是癌症的标志之一。几个常见 代谢性疾病，例如肥胖，2型糖尿病和血脂异常，已与结直肠癌有关 （CRC）风险。随着代谢组学技术的进步，数百至数千个代谢物可以 以不可知论的方式进行系统的测量。进行精心设计的前瞻性代谢组研究 癌症，新型风险相关的生物标志物的鉴定将为癌症病因和 相关的生物途径。评估代谢产物的常规研究成本很高，可能遭受偏见 在观察研究中通常遇到。人们越来越认识到遗传因素 在确定许多代谢产物的水平方面发挥重要作用。在此，我提出了一种经济高效的方法 系统地评估代谢物的血浆水平与CRC风险之间的关联。我也是 纳入分子/遗传流行病学，因果推断，癌症生物学和 在此应用中，代谢和人口遗传学，以实现我的研究和职业目标 以下。拟议的研究由四个目标组成。在AIM 1中，我将建立模型来预测代谢物 使用现有的代谢组学和高密度基因分型数据的级别和可能的数据 在范德比尔特产生。在AIM 2中，具有令人满意的预测准确性的代谢物并符合其他标准 将在三个大规模的CRC联盟中评估，样本量约为80,900例，并且 115,000个对照来评估遗传预测的代谢物水平与CRC风险的关联。在 在该奖项的R00阶段与其他调查人员合作，然后我将进行嵌套案例 - 控制研究可通过直接测量这些确认从计算机分析中确定的十种代谢产物 在三项大型队列研究中收集的诊断前血浆的代谢产物（AIM 3）。最后，我会评估 代谢物对生活方式因素之间关联的潜在调解作用，包括肥胖 （体重指数和腰线比例），体育锻炼，红肉摄入量，蔬菜摄入量，健康饮食 指数和CRC风险（AIM 4）。这项创新的研究将首次系统地寻找新颖 使用遗传仪器用于CRC风险的代谢物生物标志物，并通过直接验证已识别的关联 测量。此外，这项研究将扩大对导致CRC的基本机制的理解。 拟议的职业发展奖将帮助我建立对遗传和分子的先进知识 流行病学，癌症代谢以及CRC病因和风险评估过渡到成功 独立研究者。