• Ruyang Zhang, Chao Chen, Xuesi Dong, Sipeng Shen, Linjing Lai, Jieyu He, Dongfang You, Lijuan Lin, Ying Zhu, Hui Huang, Jiajin Chen, Liangmin Wei, Xin Chen, Yi Li, Yichen Guo, Weiwei Duan, Liya Liu, Li Su, Andrea Shafer, Thomas Fleischer, Moksnes BjaanæsMariaMDepartment of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway., Anna Karlsson, Maria Planck, Rui Wang, Johan Staaf, Åslaug Helland, Manel Esteller, Yongyue Wei, Feng Chen, and David C Christiani.
• Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China; Department of Medical Oncology, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, China.
• Chest. 2020 Aug 1; 158 (2): 808819808-819.

BackgroundDNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.Research QuestionWould screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?Study Design And MethodsBiomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.ResultsTwenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10-17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10-18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.InterpretationThe integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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