Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ

article

Jin Duan¹  Youming Lei¹  Guoli Lv¹  Yinqiang Liu¹  Wei Zhao¹  Qingmei Yang¹  Xiaona Su²  Zhijian Song³  Leilei Lu³  Yunfei Shi¹ 

[1] Department of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province;Department of Cancer Center, Daping Hospital, Army Medical University;Origimed Co. Ltd.

关键词: Lung adenocarcinoma;    LASSO Cox regression;    The Cancer Genome Atlas;    Gene set enrichment analysis;    Immune cell analysis;    Autophagy;    Gene expression omnibus database;    Multivariate cox regression analyses;    Prognosis;    Molecular biomarkers;   

DOI  :  10.7717/peerj.11074

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundLung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.MethodsIn this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.ResultsWe identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P  1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.ConclusionOur identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

【 授权许可】

CC BY