Establishment and validation of lung adenocarcinoma aging-related genes prognostic model based on TCGA database

Abstract

Abstract Background To explore the differentially expressed aging-related genes (ARGs) in lung adenocarcinoma (LUAD) and develop a prognostic model for LUAD based on aging-related genes.Methods RNA high-throughput transcriptome data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, and ARGs were obtained from the Aging Atlas database. A prognostic model for LUAD was constructed based on differentially expressed aging-related genes in LUAD and validated. A nomogram chart and calibration curve were further constructed to explore the clinical application value of the model.Results A total of 80 differentially expressed aging-related genes were obtained by Venn diagram analysis. Seven differentially expressed ARGs with independent prognostic significance were screened by univariate and multivariate Cox regression analysis. LASSO regression analysis was performed on the seven genes to construct a prognostic model for LUAD. Kaplan-Meier survival curve analysis showed that compared with the low-risk group, the high-risk group was significantly associated with poor overall survival (OS) and the difference was statistically significant (P < 0.05). The area under the receiver operating characteristic (ROC) curve for 1, 3, and 5 years were 0.706, 0.725, and 0.642 respectively, indicating that the risk model had high sensitivity and specificity. Univariate and multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for LUAD. The calibration curve and nomogram chart of gene risk score were further constructed, and the overall consistency of the calibration curve nomogram chart was 0.729, indicating that the model had high accuracy in predicting outcomes.Conclusion The risk model constructed based on differentially expressed ARGs in this study can serve as a prognostic feature for LUAD, providing a reference for individualized treatment of LUAD patients.