Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma

Abstract

Background. Lung cancer is emerging as one of most deadly diseases, and the mortality rate was still high with 5-year overall survival rate less than 20%. Aging is referred as protumorigenic state, and it plays a significant role in cancer development. Methods. Molecular subtype of lung cancer was identified by consensus cluster analysis. Prognostic signature was constructed using LASSO cox regression analysis. CeRNA network was constructed to explore lncRNA-miRNA-mRNA regulatory axis. Results. A total of 27 differentially expressed aging-related genes (ARGs) were obtained in LUAD. Three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy were identified. We also developed an aging-related prognostic signature that had a better performance in predicting the1-year, 3-year, and 5-year overall survival of LUAD. Further analysis suggested a significant correlation between prognostic signature gene expression and clinical stage, immune infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also identified the lncRNA UCA1/miR-143-3p/CDK1 regulatory axis in LUAD. Conclusion. Our study identified three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy. We also developed an aging-related prognostic signature that had a good performance in the prognosis of LUAD.