Creation of a Prognostic Risk Prediction Model for Lung Adenocarcinoma Based on Gene Expression, Methylation, and Clinical Characteristics

BACKGROUND This study aimed to identify important marker genes in lung adenocarcinoma (LACC) and establish a prognostic risk model to predict the risk of LACC in patients. MATERIAL AND METHODS Gene expression and methylation profiles for LACC and clinical information about cases were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between cancer and control groups were selected through meta-analysis. Pearson coefficient correlation analysis was performed to identify intersections between DEGs and DMGs and a functional analysis was performed on the genes that were correlated. Marker genes and clinical factors significantly related to prognosis were identified using univariate and multivariate Cox regression analyses. Risk prediction models were then created based on the marker genes and clinical factors. RESULTS In total, 1975 DEGs and 2095 DMGs were identified. After comparison, 16 prognosis-related genes (EFNB2, TSPAN7, INPP5A, VAMP2, CALML5, SNAI2, RHOBTB1, CKB, ATF7IP2, RIMS2, RCBTB2, YBX1, RAB27B, NFATC1, TCEAL4, and SLC16A3) were selected from 265 overlapping genes. Four clinical factors (pathologic N [node], pathologic T [tumor], pathologic stage, and new tumor) were associated with prognosis. The prognostic risk prediction models were constructed and validated with other independent datasets. CONCLUSIONS An integrated model that combines clinical factors and gene markers is useful for predicting risk of LACC in patients. The 16 genes that were identified, including EFNB2, TSPAN7, INPP5A, VAMP2, and CALML5, may serve as novel biomarkers for diagnosis of LACC and prediction of disease prognosis.