Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

被引:36
作者
Wang, Yanfang [1 ]
Deng, Haowen [2 ]
Xin, Shan [1 ,3 ]
Zhang, Kai [4 ]
Shi, Run [1 ]
Bao, Xuanwen [5 ,6 ]
机构
[1] Ludwig Maximilians Univ Munchen, Munich, Germany
[2] Tech Univ Munich, Chair Comp Aided Med Procedures & Augmented Real, Munich, Germany
[3] German Res Ctr Environm Hlth, Helmholtz Ctr Munich, Inst Mol Toxicol & Pharmacol, Neuherberg, Germany
[4] Zhejiang Univ, Sch Med, Dept Cardiol, Sir Run Run Shaw Hosp, Hangzhou, Zhejiang, Peoples R China
[5] German Res Ctr Environm Hlth, Helmholtz Ctr Munich, Inst Radiat Biol, Neuherberg, Germany
[6] Tech Univ Munich, Munich, Germany
关键词
LUAD; DNA methylation; regularized logistic regression; recursive feature elimination; LASSO Cox regression; metastasis; GENE-EXPRESSION; CANCER; EGFR; EPIDEMIOLOGY; INACTIVATION; ASSOCIATION; MUTATIONS; DIAGNOSIS; SURVIVAL; HEAD;
D O I
10.3389/fgene.2019.00349
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TOGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods.
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页数:13
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