基于1H-NMR指纹谱的体液代谢特征构建大肠癌早期预警的可视化预测模型

构建精准高效的早期筛查方法是提高大肠癌防控效果的关键。基于核磁共振的代谢组学已被证明可以准确测定大肠癌的组织代谢特征。前期我们运用核磁共振分别检测了大肠癌患者的粪便、血清及尿液，发现三组体液代谢均可反映癌组织的代谢紊乱，这为大肠癌的体液代谢筛查提供了依据。但是，任何单一体液代谢只能部分呈现机体的代谢状态，如能构建多源体液代谢的“全景式预测模型”，有望更精准预警早期大肠癌。本项目拟采用组织和体液代谢贯穿研究策略，运用高分辨核磁共振系统分析早期大肠癌和癌前病变的粪便、血清、尿液及癌组织代谢，以癌组织代谢作锚定挖掘体液代谢特征标签，进而应用回归方法构建大肠癌早期预警的多源体液代谢可视化模型，最后通过临床样本的回顾性验证及前瞻性预测，对照肠镜和传统筛查手段，确证模型在大肠癌筛查中的临床应用价值。项目实施有望构筑简便无创、准确性高、筛检窗口前移的体液代谢可视化预测模型，为大肠癌的筛查防治提供新策略。

It is essential to obtain an accurate and noninvasive molecule method for early warning and optimum prevention of colorectal cancer (CRC). Proton nuclear magnetic resonance (1H-NMR) spectroscopy-based metabolomics has been shown to be accurately capable of determining the metabolic characteristics of colorectal tumor tissue. Recently, we identified distinct NMR-based fecal, serum and urine metabolic signatures respectively, which were linked to the metabolic profiles of colorectal-cancerous tissues. Our findings have highlighted the potential utility of NMR-based biofluids metabolomics fingerprinting as noninvasive predictors of earlier diagnosis in CRC patients. However, the single biofluids metabolism can not fully present the metabolic characteristics of the whole body. If an optimal metabolic model through a combination of the biomarkers in fecal extracts, serum and urine can be constructed, it is possible to achieve a comprehensive metabolic information of CRC and its precancerosis, therefore improving its non-invasive screening efficiency. In this study, fecal extracts, urine and serum specimens representing the healthy, precancerous and early stage of CRC individuals will be examined using high-resolution 600 MHz 1H NMR technique. Furthermore, the paralleled patient-matched metabolites of early stage of CRC tumor tissues and their adjacent non-cancerous tissues will be investigated, which would be used as references to determine biofluids metabolic biomarkers. Pattern recognition will be applied on NMR processed data to acquire the detailed metabolic information, and the predicting metabolic model through a combination of the biomarkers in fecal extracts, serum and urine can be constructed using multiple regression analysis. Finally, the diagnostic accuracy of the predicting metabolic model will be validated through retrospective verification and prospective prediction in multicentre clinical samples, as well as in comparison with colonoscopy and traditional clinical screening methods. Successful performance of this study could help to build up a new screening and diagnostic method, which is non-invasive, inexpensive, simple, and has high sensitivity and specificity to screen for early stage of CRC.