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

Zhang, Ruyang ; Chen, Chao ; Dong, Xuesi ; Shen, Sipeng ; Lai, Linjing ; He, Jieyu ; You, Dongfang ; Lin, Lijuan ; Zhu, Ying and Huang, Hui , et al. (2020) In Chest 158(2). p.808-819
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
early stage, interaction, multiomics, non-small cell lung cancer, prognostic score
in
Chest
volume
158
issue
2
pages
12 pages
publisher
American College of Chest Physicians
external identifiers
  • scopus:85088366978
  • pmid:32113923
ISSN
0012-3692
DOI
10.1016/j.chest.2020.01.048
language
English
LU publication?
yes
id
a120590c-3ffc-45aa-a01d-b850d9143f51
date added to LUP
2020-08-04 10:39:21
date last changed
2020-09-23 08:13:53
@article{a120590c-3ffc-45aa-a01d-b850d9143f51,
  abstract     = {<p>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<sup>–17</sup>) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10<sup>–18</sup>) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC<sub>3 year</sub>, 0.88 [95% CI, 0.83-0.93]; and AUC<sub>5 year</sub>, 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.</p>},
  author       = {Zhang, Ruyang and Chen, Chao and Dong, Xuesi and Shen, Sipeng and Lai, Linjing and He, Jieyu and You, Dongfang and Lin, Lijuan and Zhu, Ying and Huang, Hui and Chen, Jiajin and Wei, Liangmin and Chen, Xin and Li, Yi and Guo, Yichen and Duan, Weiwei and Liu, Liya and Su, Li and Shafer, Andrea and Fleischer, Thomas and Moksnes Bjaanæs, Maria and Karlsson, Anna and Planck, Maria and Wang, Rui and Staaf, Johan and Helland, Åslaug and Esteller, Manel and Wei, Yongyue and Chen, Feng and Christiani, David C.},
  issn         = {0012-3692},
  language     = {eng},
  number       = {2},
  pages        = {808--819},
  publisher    = {American College of Chest Physicians},
  series       = {Chest},
  title        = {Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects},
  url          = {http://dx.doi.org/10.1016/j.chest.2020.01.048},
  doi          = {10.1016/j.chest.2020.01.048},
  volume       = {158},
  year         = {2020},
}