Wen Shi, Li Zhishui, Feng Jianghua, Bai Jianxi, Lin Xianchao, Huang Heguang
Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
Cancer Sci. 2016 Jun;107(6):836-45. doi: 10.1111/cas.12939. Epub 2016 May 17.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and is difficult to diagnose in the early phase. This study was aimed at obtaining the metabolic profiles and characteristic metabolites of pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues from Sprague-Dawley (SD) rats to establish metabonomic methods used in the early diagnosis of PDAC. In the present study, the animal models were established by embedding 7,12-dimethylbenzanthracene (DMBA) in the pancreas of SD rats to obtain PanIN and PDAC tissues. After the preprocessing of tissues, (1) H nuclear magnetic resonance (NMR) spectroscopy combined with multivariate and univariate statistical analysis was applied to identify the potential metabolic signatures and the corresponding metabolic pathways. Pattern recognition models were successfully established and differential metabolites, including glucose, amino acids, carboxylic acids and coenzymes, were screened out. Compared with the control, the trends in the variation of several metabolites were similar in both PanIN and PDAC. Kynurenate and methionine levels were elevated in PanIN but decreased in PDAC, thus, could served as biomarkers to distinguish PanIN from PDAC. Our results suggest that NMR-based techniques combined with multivariate statistical analysis can distinguish the metabolic differences among PanIN, PDAC and normal tissues, and, therefore, present a promising approach for physiopathologic metabolism investigations and early diagnoses of PDAC.
胰腺导管腺癌（PDAC）是最恶性的肿瘤之一，早期难以诊断。本研究旨在获取来自Sprague-Dawley（SD）大鼠的胰腺上皮内瘤变（PanIN）和PDAC组织的代谢谱及特征性代谢物，以建立用于PDAC早期诊断的代谢组学方法。在本研究中，通过将7,12-二甲基苯并蒽（DMBA）植入SD大鼠胰腺来建立动物模型，以获取PanIN和PDAC组织。组织预处理后，应用（1）氢核磁共振（NMR）光谱结合多变量和单变量统计分析来识别潜在的代谢特征及相应的代谢途径。成功建立了模式识别模型，并筛选出包括葡萄糖、氨基酸、羧酸和辅酶在内的差异代谢物。与对照组相比，PanIN和PDAC中几种代谢物的变化趋势相似。犬尿氨酸和蛋氨酸水平在PanIN中升高，但在PDAC中降低，因此可作为区分PanIN和PDAC的生物标志物。我们的结果表明，基于NMR的技术结合多变量统计分析可以区分PanIN、PDAC和正常组织之间的代谢差异，因此为PDAC的生理病理代谢研究和早期诊断提供了一种有前景的方法。
