Tate A R, Foxall P J, Holmes E, Moka D, Spraul M, Nicholson J K, Lindon J C
Biological Chemistry, Division of Biomedical Sciences, Imperial College School of Medicine, University of London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ UK.
NMR Biomed. 2000 Apr;13(2):64-71. doi: 10.1002/(sici)1099-1492(200004)13:2<64::aid-nbm612>3.0.co;2-x.
The technique of magic angle spinning (MAS) high resolution (1)H NMR spectroscopy applied to intact tissues provides excellent peak resolution and thus much biochemical information. The use of computer-based pattern recognition techniques to classify human renal cortex tissue samples as normal or tumour based on their (1)H MAS NMR spectra has been investigated. In this preliminary study of 22 paired control and tumour samples, exploratory data analysis using principal components based on NMR spectral intensities showed clear separation of the two classes. Furthermore, using the supervised method of linear discriminant analysis, based on individual data point intensities or on integrated spectral regions, it was possible to distinguish between the normal and tumour kidney cortex tissue with 100% accuracy, including a single example of a metastatic tumour from a primary lung carcinoma. A tumour sample from the collecting duct of the kidney showed a different NMR spectral profile, and pattern recognition indicated that this sample did not classify with the cortical tumours.
将魔角旋转（MAS）高分辨率氢核磁共振（1H NMR）光谱技术应用于完整组织，可提供出色的峰分辨率，从而获得大量生化信息。人们研究了使用基于计算机的模式识别技术，根据氢MAS NMR光谱将人类肾皮质组织样本分类为正常或肿瘤样本。在这项对22对对照和肿瘤样本的初步研究中，基于NMR光谱强度使用主成分的探索性数据分析显示，两类样本有明显区分。此外，使用基于单个数据点强度或积分光谱区域的线性判别分析监督方法，能够以100%的准确率区分正常和肿瘤肾皮质组织，其中包括一例来自原发性肺癌的转移性肿瘤。来自肾集合管的一个肿瘤样本显示出不同的NMR光谱特征，模式识别表明该样本不属于皮质肿瘤类别。
