Research Paper Volume 11, Issue 16 pp 6312—6335
Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
- 2 Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- 3 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- 4 China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
- 5 Department of Medical Oncology, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, China
- 6 Clinical Metabolomics Center, China Pharmaceutical University, Nanjing 211198, China
- 7 Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- 8 Bellvitge Biomedical Research Institute and University of Barcelona, Institucio Catalana de Recerca i Estudis Avançats, Barcelona 08908, Catalonia , Spain
- 9 Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo 0424, Norway
- 10 Division of Oncology and Pathology, Department of Clinical Sciences Lund, CREATE Health Strategic Center for Translational Cancer Research, Lund University, Lund 2238, Skåne, Sweden
- 11 Institute of Clinical Medicine, University of Oslo, Oslo 0424, Norway
- 12 Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
received: May 14, 2019 ; accepted: August 10, 2019 ; published: August 21, 2019 ;https://doi.org/10.18632/aging.102189
How to Cite
Copyright © 2019 Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
LUAD: lung adenocarcinoma; NSCLC: none small cell lung cancer; OS: overall survival; FDR: false discovery rate; MRS: DNA methylation risk score; GRS: gene expression risk score; c-index: concordance index; ROC: receiver operating characteristic; AUC: area under ROC; HR: hazard ratio; CI: confident interval; VIS: variable importance score; OOB: out of bag; COSMIC: Catalogue of Somatic Mutations in Cancer.