Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

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Background: DNA 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 Question: Would 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 Methods: Biomarker 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. Results: Twenty-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. Interpretation: The 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.


  • 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
  • Maria Moksnes Bjaanæs
  • Anna Karlsson
  • Rui Wang
  • Åslaug Helland
  • Manel Esteller
  • Yongyue Wei
  • Feng Chen
  • David C. Christiani
Enheter & grupper
Externa organisationer
  • Nanjing Medical University
  • Harvard University
  • Nanjing University
  • Southeast University, Nanjing
  • University of Michigan
  • Ningbo University
  • Massachusetts General Hospital
  • Oslo university hospital
  • University of Oslo
  • Josep Carreras Leukaemia Research Institute (IJC)
  • Catalan Institution for Research and Advanced Studies
  • University of Barcelona
  • Jinling Hospital, Nanjing
  • CIBERONC Centro de Investigación Biomédica en Red Cáncer

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Cancer och onkologi


Sidor (från-till)808-819
Antal sidor12
Utgåva nummer2
StatusPublished - 2020
Peer review utfördJa