BACKGROUND 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. METHODS 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. RESULTS 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 =5.10×10-17) 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 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. CONCLUSION 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.

中文翻译：

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将表观遗传和转录生物标志物与基因-基因相互作用和主要效应结合起来的早期 NSCLC 预后评分的独立验证

背景 DNA 甲基化和基因表达是各种癌症，包括非小细胞肺癌 (NSCLC) 的有希望的生物标志物。除了生物标志物的主要作用外，复杂疾病的进展还受到基因-基因 (GxG) 相互作用的影响。使用多组学数据筛选基于主要效应或相互作用的生物标志物的功能能力可能会提高癌症预后的准确性。方法生物标志物筛选和模型验证用于构建和验证预后预测模型。NSCLC 预后相关的生物标志物是根据它们的主要效应或与两种类型的组学数据的相互作用来确定的。独立验证了包含表观遗传和转录生物标志物以及临床信息的预后评分。结果 26 对具有 GxG 相互作用的生物标志物和两种具有主效应的生物标志物与 NSCLC 生存显着相关。与仅利用临床信息的模型相比，基于表观遗传和转录生物标志物的预后模型的准确性（按受试者工作特征曲线（AUC）下面积测量）提高了 35.38%（95% CI：27.09%-42.17%， 3 年和 5 年生存率分别为 P =5.10×10-17) 和 34.85% (95% CI: 26.33%-41.87%, P =2.52×10-18)，对 NSCLC 生存率具有优越的预测能力（AUC3 年 =0.88，95% CI：0.83-0.93 和 AUC5 年 =0.89，95% CI：0.83-0.93）在独立的癌症基因组图谱 (TCGA) 人群中。GxG 相互作用使 3 年和 5 年生存率的预测准确性分别提高了 65.2% 和 91.3%。