DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (GxG) interactions. Screening the functional capacity of biomarkers based on main effects or interactions using multi-omics data may improve the accuracy of cancer prognosis.Biomarker screening and model validation was used to construct and validate a prognostic prediction model. NSCLC prognosis associated biomarkers were identified based on 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.Twenty-six pairs of biomarkers with GxG interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared to a model utilizing 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 =5.1010-17) and 34.85% (95% CI: 26.33%-41.87%, P =2.5210-18) 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 The Cancer Genome Atlas (TCGA) population. GxG interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.The integration of epigenetic and transcriptional biomarkers with main effects and GxG interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.