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Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Dong, Xuesi ; Zhang, Ruyang ; He, Jieyu ; Lai, Linjing ; Alolga, Raphael N. ; Shen, Sipeng ; Zhu, Ying ; You, Dongfang ; Lin, Lijuan and Chen, Chao , et al. (2019) In Aging 11(16). p.6312-6335
Abstract

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis.... (More)

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
DNA methylation, early-stage, gene expression, lung adenocarcinoma, prognostic prediction
in
Aging
volume
11
issue
16
pages
24 pages
publisher
Impact Journals
external identifiers
  • scopus:85071788907
  • pmid:31434796
ISSN
1945-4589
DOI
10.18632/aging.102189
language
English
LU publication?
yes
id
3d812759-1784-441f-9e44-605fa0ee35bf
date added to LUP
2019-09-18 13:44:40
date last changed
2024-04-16 20:16:19
@article{3d812759-1784-441f-9e44-605fa0ee35bf,
  abstract     = {{<p>Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.</p>}},
  author       = {{Dong, Xuesi and Zhang, Ruyang and He, Jieyu and Lai, Linjing and Alolga, Raphael N. and Shen, Sipeng and Zhu, Ying and You, Dongfang and Lin, Lijuan and Chen, Chao and Zhao, Yang and Duan, Weiwei and Su, Li and Shafer, Andrea and Salama, Moran and Fleischer, Thomas and Bjaanæs, Maria Moksnes 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         = {{1945-4589}},
  keywords     = {{DNA methylation; early-stage; gene expression; lung adenocarcinoma; prognostic prediction}},
  language     = {{eng}},
  number       = {{16}},
  pages        = {{6312--6335}},
  publisher    = {{Impact Journals}},
  series       = {{Aging}},
  title        = {{Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma}},
  url          = {{http://dx.doi.org/10.18632/aging.102189}},
  doi          = {{10.18632/aging.102189}},
  volume       = {{11}},
  year         = {{2019}},
}