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.
Highlights
Lung cancer is the leading cause of cancer-related deaths worldwide (18.4% of total cancer deaths), with an estimated 1.76 million deaths every year [1]
Ranger screened out 62 DNA methylation probes in the discovery phase according to variable importance score (VIS) (Figure 1A)
The 12 DNA methylation probes were significantly associated with prognosis after correction for multiple comparisons (Supplementary Table 1)
Summary
Lung cancer is the leading cause of cancer-related deaths worldwide (18.4% of total cancer deaths), with an estimated 1.76 million deaths every year [1]. Early-stage LUAD patients have a relatively superior prognosis, but even with complete surgical resection, nearly 33%–52% of patients still die from cancer within five years [3]. Great efforts have been put into using gene expression or DNA methylation data to predict the prognosis of non-small cell lung cancer (NSCLC) [5,6,7]. Several studies have explored prognostic prediction models for NSCLC or LUAD using molecular biomarkers from single omics data, providing opportunities to identify patients with heterogeneous prognoses [8, 9]. Limited prognostic models have focused on early-stage LUAD, especially with transomics predictors. There may be significant possibilities to develop a trans-omics prognostic model for early-stage LUAD
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