TY - JOUR
T1 - Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
AU - Dong, Xuesi
AU - Zhang, Ruyang
AU - He, Jieyu
AU - Lai, Linjing
AU - Alolga, Raphael N.
AU - Shen, Sipeng
AU - Zhu, Ying
AU - You, Dongfang
AU - Lin, Lijuan
AU - Chen, Chao
AU - Zhao, Yang
AU - Duan, Weiwei
AU - Su, Li
AU - Shafer, Andrea
AU - Salama, Moran
AU - Fleischer, Thomas
AU - Bjaanæs, Maria Moksnes
AU - Karlsson, Anna
AU - Planck, Maria
AU - Wang, Rui
AU - Staaf, Johan
AU - Helland, Åslaug
AU - Esteller, Manel
AU - Wei, Yongyue
AU - Chen, Feng
AU - Christiani, David C.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - DNA methylation
KW - early-stage
KW - gene expression
KW - lung adenocarcinoma
KW - prognostic prediction
U2 - 10.18632/aging.102189
DO - 10.18632/aging.102189
M3 - Article
C2 - 31434796
AN - SCOPUS:85071788907
SN - 1945-4589
VL - 11
SP - 6312
EP - 6335
JO - Aging
JF - Aging
IS - 16
ER -