高分辨魔角旋转核磁共振和主成分分析研究人类低级星形细胞瘤和脑膜瘤的代谢组特征

采用高分辨魔角旋转核磁共振（HRMAS ^1H NMR）技术结合主成分分析（PCA）方法研究了39例人体脑肿瘤组织的代谢组特征．39例肿瘤样本分别来自39个脑肿瘤患者，包括15例低级星形细胞瘤，13例纤维型脑膜瘤和11例过渡型脑膜瘤．核磁共振波谱分析结果表明，脑肿瘤组织的代谢组中丰要含有脂肪酸、乳酸、胆碱代谢物（如胆碱、磷酸胆碱和甘油磷酸胆碱）、氯基酸（如丙氨酸、谷氨酸、谷氮酰胺、牛磺酸）、N-乙酰天门冬氨酸（NAA）和谷胱甘肽等代谢物．通过对核磁共振谱进行主成分分析（PCA），发现低级星形细胞瘤和脑膜瘤的代谢组之间具有明显的差异，而在过渡型和纤维型两个亚类脑膜瘤之间该差别相对较小．与脑膜瘤相比，低级星形细胞瘤中甘油磷酸胆碱、磷酸胆碱、肌醇与肌酸的含量较高，而丙氨酸、谷氨酸、谷氨酰胺、谷胱甘肽和牛磺酸的含量较低．NAA的含量在低级星形细胞瘤中尽管较低但能观察到，而脑膜瘤中却未发现NAA的信号．结果衷明，HRMAS ^1H NMR和多变量统计分析相结合的组织代谢组学方法，不仅能有效区分不同类型的脑肿瘤，而且还可以为脑肿瘤提供丰富的代谢组信息，这些信息对研究肿瘤发生发展的机制具有潜在的意义．

Metabonomic Characterization of The Low-grade Human Astrocytomas and Meningiomas Using Magic-angle Spinning 1H Nuclear Magnetic Resonance Spectroscopy and Principal Component Analysis

Metabolic characteristics of 39 human brain tumor tissues, including 15 astrocytomas, 13 fibroblastic meningiomas and 11 transitional meningiomas from 39 individual patients, have been studied using high resolution magic-angle spinning (HRMAS) ¹H NMR spectroscopy in conjunction with principal component analysis (PCA). With rich metabolite information, ¹H NMR spectra showed that the tumor-tissue metabonome was dominated by lipids, lactate, myo-inositol, creatine, choline metabolites such as choline, phosphocholine and glycerophosphocholine, amino acids such as alanine, glutamate, glutamine, taurine, N-acetyl-aspartate and glutathione. PCA of the tumor NMR spectra clearly showed metabonomic differences between low-grade astrocytomas and meningiomas whereas such differences were more moderate between fibroblastic and transitional meningiomas. Compared with meningiomas, the low-grade astrocytomas had higher levels of glycerophosphocholine, phosphocholine, myo-inositol and creatine but lower levels of alanine, glutamate, glutamine, glutathione and taurine. The N-acetyl-aspartate level was low but detectable in low-grade astrocytomas whereas it was not detectable in meningiomas. It is concluded that tissue metabonomics technology consisting of HRMAS ¹H NMR spectroscopy and multivariate data analysis (MVDA) offers a useful tool (1) for distinguishing different types of brain tumors, (2) for providing the metabolic information for human brain tumors, which are potentially useful for understanding biochemistry of tumor progression.