Please use this identifier to cite or link to this item:
Title: Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
Author: Dong, Xuesi
Zhang, Ruyang
He, Jieyu
Lai, Linjing
Alolga, Raphael N.
Shen, Sipeng
Zhu, Ying
You, Dongfang
Lin, Lijuan
Chen, Chao
Zhao, Yang
Duan, Weiwei
Su, Li
Shafer, Andrea
Salama, Moran
Fleischer, Thomas
Bjaanæs, Maria Moksnes
Karlsson, Anna
Planck, Maria
Wang, Rui
Staaf, Johan
Helland, Åslaug
Esteller, Manel
Wei, Yongyue
Chen, Feng
Christiani, David C.
Keywords: ADN
Expressió gènica
Càncer de pulmó
Gene expression
Lung cancer
Issue Date: 21-Aug-2019
Publisher: Impact Journals
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. 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.
Note: Reproducció del document publicat a:
It is part of: Aging, 2019, vol. 11, num. 16, p. 6312-6335
Related resource:
ISSN: 1945-4589
Appears in Collections:Articles publicats en revistes (Ciències Fisiològiques)

Files in This Item:
File Description SizeFormat 
695390.pdf4.5 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons