Tumour class prediction and discovery by microarray-based DNA methylation analysis -: art. no. e21
Tumour class prediction and discovery by microarray-based DNA methylation analysis -: art. no. e21
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DOI：
10.1093/nar/30.5.e21
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发表时间：
2002-03-01
影响因子：
14.9
通讯作者：
Olek, A
中科院分区：
文献类型：
--
作者：
Adorján, P;Distler, J;Olek, A
CpG位点的异常DNA甲基化是癌症中最早和最常见的改变之一。一些研究表明，异常甲基化以肿瘤类型特异性方式发生。然而，大规模的候选基因的分析，迄今为止，由于缺乏高通量的甲基化检测方法受到阻碍。我们开发了第一个基于微阵列的技术，该技术允许对选定的CpG二核苷酸进行全基因组评估，并对每个位点的甲基化进行定量。在来自四种不同的人类肿瘤类型和相应的健康对照的76个样品中筛选了数百个CpG位点。鉴别CpG二核苷酸用于不同的组织类型区分，并用于使用机器学习技术以高准确度预测未知样品的肿瘤类别。一些CpG二核苷酸与恶性肿瘤的进展相关，而另一些则以组织特异性方式甲基化，与恶性肿瘤无关。我们的研究结果表明，甲基化模式的全基因组分析结合监督和无监督机器学习技术构成了一个强大的新工具来分类人类癌症。
Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers.