Cytosine methylation profiles as a molecular marker in non-small cell lung cancer
Cytosine methylation profiles as a molecular marker in non-small cell lung cancer
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DOI：
10.1158/0008-5472.can-06-0400
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发表时间：
2006-11-15
期刊：
影响因子：
11.2
通讯作者：
van den Boom, Dirk
中科院分区：
文献类型：
--
作者：
Ehrich, Mathias;Field, John K.;van den Boom, Dirk
在不同类型的肺癌中经常观察到异常的启动子甲基化。表观遗传修饰被认为发生在疾病的临床发作之前，因此作为早期检测标记物具有很大的前景。DNA甲基化的广泛分析受到以下方法的阻碍，这些方法要么劳动强度太大，无法进行大规模研究，要么没有足够的定量来测量甲基化程度的细微变化。我们使用一种新的定量DNA甲基化分析技术完成了一项大规模的胞嘧啶甲基化分析研究，涉及96例肺癌患者的47个基因启动子区域。每个人贡献了一个肺癌标本和相应的相邻正常组织。该研究确定了6个基因在正常组织和肿瘤组织之间的甲基化差异具有统计学意义（P < 10（-6））。我们使用无监督的层次聚类算法探索了定量甲基化数据。数据分析显示，甲基化模式区分正常与肿瘤组织。为了验证我们的方法，我们划分样本来训练分类器并测试其性能。我们能够以> 95%的灵敏度和特异性区分正常组织和肺癌组织。这些结果表明，定量胞嘧啶甲基化谱可用于鉴定肺癌的分子分类标志物。
Aberrant promoter methylation is frequently observed in different types of lung cancer. Epigenetic modifications are believed to occur before the clinical onset of the disease and hence hold a great promise as early detection markers. Extensive analysis of DNA methylation has been impeded by methods that are either too labor intensive to allow large-scale studies or not sufficiently quantitative to measure subtle changes in the degree of methylation. We used a novel quantitative DNA methylation analysis technology to complete a large-scale cytosine methylation profiling study involving 47 gene promoter regions in 96 lung cancer patients. Each individual contributed a lung cancer specimen and corresponding adjacent normal tissue. The study identified six genes with statistically significant differences in methylation between normal and tumor tissue (P < 10(-6)). We explored the quantitative methylation data using an unsupervised hierarchical clustering algorithm. The data analysis revealed that methylation patterns differentiate normal from tumor tissue. For validation of our approach, we divided the samples to train a classifier and test its performance. We were able to distinguish normal from lung cancer tissue with > 95% sensitivity and specificity. These results show that quantitative cytosine methylation profiling can be used to identify molecular classification markers in lung cancer.