Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma - Wang, Yanfang; Deng, Haowen; Xin, Shan; Zhang, Kai; Shi, Run; Bao, Xuanwen (Frontiers in Genetics, 2019)
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 (TCGA) 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 6198 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 15 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 6198-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 15-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved the value 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.