Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC).
Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC).
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DOI：
10.1186/s13148-014-0035-3
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发表时间：
2015
影响因子：
5.7
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Guo S;Yan F;Xu J;Bao Y;Zhu J;Wang X;Wu J;Li Y;Pu W;Liu Y;Jiang Z;Ma Y;Chen X;Xiong M;Jin L;Wang J
DNA甲基化被认为是肺癌诊断的一个有希望的生物标志物。然而，寻找甲基化生物标志物的最佳组合以获得最大的诊断性能是一个巨大的挑战。在这项研究中，我们开发了一组DNA甲基化生物标志物，并在一个大型中国汉族NSCLC回顾性队列中验证了它们对非小细胞肺癌（NSCLC）的诊断效率。在发现阶段收集了三个高通量DNA甲基化微阵列数据集（458个样本）。在归一化、批量效应消除和整合之后，通过留一法SVM（支持向量机）特征选择程序确定显著差异甲基化基因和生物标志物的最佳组合。然后，通过甲基化状态确定的单核苷酸引物延伸技术（MSD-SNUPET）在150对NSCLC/正常组织的独立组中检查候选启动子。四个统计模型与五重交叉验证被用来评估的歧视性算法的性能。贝叶斯树模型的敏感性、特异性和准确性分别为86.3%、95.7%和91%。logistic回归模型包括AGTR 1、GALR 1、SLC 5A 8、ZMYND 10和NTSR 1处的五个基因甲基化特征，并针对年龄、性别和吸烟进行了调整，显示出稳健的性能，其中灵敏度、特异性、准确性和曲线下面积（AUC）分别为78%、97%、87%和0.91。总之，一个高通量的DNA甲基化微阵列数据集，然后消除批次效应可以是一个很好的策略，以发现最佳的DNA甲基化诊断面板。AGTR 1、GALR 1、SLC 5A 8、ZMYND 10和NTSR 1的甲基化谱可能是一种有效的基于甲基化的NSCLC诊断方法。本文的在线版本（doi：10.1186/s13148-014-0035-3）包含补充材料，可供授权用户使用。
DNA methylation was suggested as the promising biomarker for lung cancer diagnosis. However, it is a great challenge to search for the optimal combination of methylation biomarkers to obtain maximum diagnostic performance. In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency for non-small cell lung cancer (NSCLC) in a large Chinese Han NSCLC retrospective cohort. Three high-throughput DNA methylation microarray datasets (458 samples) were collected in the discovery stage. After normalization, batch effect elimination and integration, significantly differentially methylated genes and the best combination of the biomarkers were determined by the leave-one-out SVM (support vector machine) feature selection procedure. Then, candidate promoters were examined by the methylation status determined single nucleotide primer extension technique (MSD-SNuPET) in an independent set of 150 pairwise NSCLC/normal tissues. Four statistical models with fivefold cross-validation were used to evaluate the performance of the discriminatory algorithms. The sensitivity, specificity and accuracy were 86.3%, 95.7% and 91%, respectively, in Bayes tree model. The logistic regression model incorporated five gene methylation signatures at AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, adjusted for age, sex and smoking, showed robust performances in which the sensitivity, specificity, accuracy, and area under the curve (AUC) were 78%, 97%, 87%, and 0.91, respectively. In summary, a high-throughput DNA methylation microarray dataset followed by batch effect elimination can be a good strategy to discover optimal DNA methylation diagnostic panels. Methylation profiles of AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, could be an effective methylation-based assay for NSCLC diagnosis. The online version of this article (doi:10.1186/s13148-014-0035-3) contains supplementary material, which is available to authorized users.
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影响因子：
3.6
作者：
Heakal, Yasser;Woll, Matthew P.;Kester, Mark
通讯作者：
Kester, Mark
影响因子：
11.2
作者：
Nikolaidis G;Raji OY;Markopoulou S;Gosney JR;Bryan J;Warburton C;Walshaw M;Sheard J;Field JK;Liloglou T
通讯作者：
Liloglou T
影响因子：
--
作者：
Ueno, M;Toyota, M;Imai, K
通讯作者：
Imai, K
影响因子：
2.9
作者：
Park, Jong Y.;Helm, James F.;Malafa, Mokenge P.
通讯作者：
Malafa, Mokenge P.
APC启动子甲基化对非小细胞肺癌诊断作用的定量评估
影响因子：
5.7
作者：
Guo S;Tan L;Pu W;Wu J;Xu K;Wu J;Li Q;Ma Y;Xu J;Jin L;Wang J
通讯作者：
Wang J