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通过毛细管气相色谱法和模式识别分析对人类癌细胞进行分类。

Classification of human cancer cells by means of capillary gas chromatography and pattern recognition analysis.

作者信息

Jellum E, Bjørnson I, Nesbakken R, Johansson E, Wold S

出版信息

J Chromatogr. 1981 Nov 6;217:231-7. doi: 10.1016/s0021-9673(00)88077-2.

Abstract

The metabolic profiles of brain biopsies obtained at surgery were recorded using capillary gas chromatography (GC). About 160 peaks were seen, of which 105 were used for data analysis. Three classes of brain tissue were examined: normal cerebral cortex, pituitary tumours and " brain" tumours. Pattern recognition analyses of the GC profiles using the SIMCA multivariate programme clearly resolved normal brain tissue from the tumours. Subclassification of the different tumours was more difficult, probable because the number of samples in each tumour class was too small. High-resolution two-dimensional electrophoresis separated the brain biopsies into several hundred different proteins. The combined use of the latter technique and capillary GC-mass spectrometry and pattern recognition analyses gives the possibility of the classification of diseased cells based solely on differences in their biochemical compositions.

摘要

手术时获取的脑活检组织的代谢图谱通过毛细管气相色谱法(GC)进行记录。观察到约160个峰,其中105个用于数据分析。检查了三类脑组织:正常大脑皮层、垂体肿瘤和“脑”肿瘤。使用SIMCA多变量程序对GC图谱进行模式识别分析,可清晰地将正常脑组织与肿瘤区分开来。对不同肿瘤进行亚分类则更为困难,可能是因为每个肿瘤类别中的样本数量过少。高分辨率二维电泳将脑活检组织分离成数百种不同的蛋白质。后一种技术与毛细管GC-质谱联用以及模式识别分析的结合,使得仅根据病变细胞生化组成的差异对其进行分类成为可能。

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