A Novel Risk Model Based on Autophagy Pathway Related Genes for Survival Prediction in Lung Adenocarcinoma

Abstract

Background: Autophagy has a principal role in mediating tumor cell metabolism. However, the role of autophagy-pathway-related genes (APRGs) as prognostic markers remains obscure in lung adenocarcinoma (LUAD). More potential prognostic biomarkers are needed to deepen our understanding to explore the prognostic role of APRGs in LUAD. Material/Methods: We used The Cancer Genome Atlas (TCGA) database to identify differentially expressed APRGs. Cox proportional hazard regression was used to identify prognostic APRGs, and then a risk model was constructed. The efficacy of the risk model was confirmed using a testing group. Lastly, we explored mutational signatures of prognostic of APRGs. T-tests were used to analyze all the expression patterns of genes by SPSS 19.0. Results: Using TCGA database, 5 differently expressed APRGs were identified in LUAD patients, and functional enrichment analyze of the genes that were closely associated with the survival status in LUAD patients. Cox proportional hazard regression was facilitated to identify 9 APRGs (CCR2, LAMP1, RELA, ATG12, ATG9A, NCKAP1, ATG10, DNAJB9, and MBTPS2). Multivariate Cox proportional hazards regression analyses further identified 5 key prognostic APRGs (CCR2, LAMP1, RELA, ATG12, and MBTPS2) that were closely related to the survival status in LUAD. Then the prognostic scores based on the 5 genes as independent prognostic indicators were constructed for overall survival (OS) of LUAD patients; area under the curve (AUC) values >0.70 (all P<0.05). The efficacy of prognostic scores was confirmed by data from the testing group and showed significant differences between the low-risk and the high-risk groups for OS (P<0.05). Conclusions: The risk model based on the construction of 5 APRGs can predict the prognosis of patients with LUAD, which may potentially predict prognostic signatures for LUAD.