Metabolic Profiling of Human Colorectal Cancer Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy and Gas Chromatography Mass Spectrometry (GC/MS)
Metabolic Profiling of Human Colorectal Cancer Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy and Gas Chromatography Mass Spectrometry (GC/MS)
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DOI：
10.1021/pr8006232
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发表时间：
2009-01-01
影响因子：
4.4
通讯作者：
Keun, Hector C.
中科院分区：
文献类型：
--
作者：
Chan, Eric Chun Yong;Koh, Poh Koon;Keun, Hector C.
目前临床上对结直肠癌（CRC）的分期和诊断主要依赖于TNM或杜克系统。这个临床病理学阶段是一个粗略的预后指南，因为它部分反映了晚期癌症诊断的延迟，并且对肿瘤的生物学特征几乎没有了解。我们假设，结肠粘膜的总体代谢谱（代谢组学/代谢组学）将定义代谢特征，不仅可以区分恶性和正常粘膜，而且可以区分结直肠癌的解剖学和临床病理学特征。我们应用高分辨率魔角旋转核磁共振（HR-MAS NMR）和气相色谱质谱（GC/MS）分析31例结直肠癌患者活检结直肠肿瘤及其匹配的正常粘膜中的代谢物。从两种分析方法获得的代谢谱生成的正交偏最小二乘判别分析（OPLS-DA）模型可以稳健地区分正常和恶性样本（Q（2）> 0.50，受试者操作特征（ROC）AUC > 0.95，使用7倍交叉验证）。使用两个分析平台共鉴定了31种标志物代谢物。这些代谢物中的大多数与CRC中的预期代谢紊乱相关，包括组织缺氧、糖酵解、核苷酸生物合成、脂质代谢、炎症和类固醇代谢升高。OPLS-DA模型显示，通过HR-MAS NMR获得的代谢物谱可以进一步区分结肠癌和直肠癌（Q（2）> 0.60，ROC AUC = 1.00，使用7倍交叉验证）。这些数据表明，CRC粘膜的代谢谱可以为CRC管理提供新的表型生物标志物。
Current clinical strategy for staging and prognostication of colorectal cancer (CRC) relies mainly upon the TNM or Duke system. This clinicopathological stage is a crude prognostic guide because it reflects in part the delay in diagnosis in the case of an advanced cancer and gives little insight into the biological characteristics of the tumor. We hypothesized that global metabolic profiling (metabonomics/metabolomics) of colon mucosae would define metabolic signatures that not only discriminate malignant from normal mucosae, but also could distinguish the anatomical and clinicopathological characteristics of CRC. We applied both high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) and gas chromatography mass spectrometry (GC/MS) to analyze metabolites in biopsied colorectal tumors and their matched normal mucosae obtained from 31 CRC patients. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from malignant samples (Q(2) > 0.50, Receiver Operator Characteristic (ROC) AUC > 0.95, using 7-fold cross validation). A total of 31 marker metabolites were identified using the two analytical platforms. The majority of these metabolites were associated with expected metabolic perturbations in CRC including elevated tissue hypoxia, glycolysis, nucleotide biosynthesis, lipid metabolism, inflammation and steroid metabolism. OPLS-DA models showed that the metabolite profiles obtained via HR-MAS NMR could further differentiate colon from rectal cancers (Q(2) > 0.60, ROC AUC = 1.00, using 7-fold cross validation). These data suggest that metabolic profiling of CRC mucosae could provide new phenotypic biomarkers for CRC management.