Independent validation of early-stage NSCLC prognostic scores incorporating epigenetic and transcriptional biomarkers with gene-gene interactions and main effects

dc.contributor.author
Zhang, Ruyang
dc.contributor.author
Chen, Chao
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Dong, Xuesi
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Shen, Sipeng
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Lai, Linjing
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He, Jieyu
dc.contributor.author
You, Dongfang
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Lin, Lijuan
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Zhu, Ying
dc.contributor.author
Huang, Hui
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Chen, Jiajin
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Wei, Liangmin
dc.contributor.author
Chen, Xin
dc.contributor.author
Li, Yi
dc.contributor.author
Guo, Yichen
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Duan, Weiwei
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Liu, Liya
dc.contributor.author
Su, Li
dc.contributor.author
Shafer, Andrea
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Fleischer, Thomas
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Bjaanæs, Maria Moksnes
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Karlsson, Anna
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Planck, Maria
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Wang, Rui
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Staaf, Johan
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Helland, Åslaug
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Esteller, Manel
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Wei, Yongyue
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Chen, Feng
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Christiani, David C.
dc.date.issued
2021-03-09T15:26:05Z
dc.date.issued
2021-03-09T15:26:05Z
dc.date.issued
2020-08-01
dc.date.issued
2021-03-09T15:26:05Z
dc.identifier
0012-3692
dc.identifier
https://hdl.handle.net/2445/174837
dc.identifier
700094
dc.identifier
32113923
dc.description.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 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.
dc.format
12 p.
dc.format
application/pdf
dc.language
eng
dc.publisher
American College of Chest Physicians
dc.relation
Reproducció del document publicat a: https://doi.org/10.1016/j.chest.2020.01.048
dc.relation
Chest, 2020, vol. 158, num. 2, p. 808-819
dc.relation
https://doi.org/10.1016/j.chest.2020.01.048
dc.rights
cc by (c) Zhang et al, 2020
dc.rights
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Ciències Fisiològiques)
dc.subject
Càncer de pulmó
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Cèl·lules canceroses
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Marcadors tumorals
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Epigenètica
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Lung cancer
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Cancer cells
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Tumor markers
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Epigenetics
dc.title
Independent validation of early-stage NSCLC prognostic scores incorporating epigenetic and transcriptional biomarkers with gene-gene interactions and main effects
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion


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