Research Paper Volume 11, Issue 23 pp 11440—11462

Prognostic implications of autophagy-associated gene signatures in non-small cell lung cancer

Yang Liu 1, 2, *, , Ligao Wu 3, *, , Haijiao Ao 2, , Meng Zhao 1, , Xue Leng 4, , Mingdong Liu 2, , Jianqun Ma 4, , Jinhong Zhu 1, ,

  • 1 Department of Clinical Laboratory, Biobank, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China
  • 2 Department of Clinical Oncology, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China
  • 3 Department of Pathology, BengBu Medical College, BengBu 233000, Anhui, China
  • 4 Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China
* Equal contribution

received: August 31, 2019 ; accepted: November 19, 2019 ; published: December 7, 2019 ;

https://doi.org/10.18632/aging.102544
How to Cite

Copyright © 2019 Liu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

Abbreviations

AUC: area under the curve; C-index: concordance index; EGFR-TKI: epidermal growth factor receptor tyrosine kinase inhibitor; GO: Gene Ontology; GEO: gene expression omnibus; GSEA: Gene Set Enrichment Analysis; HR: hazard ratio; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; NSCLC: non-small cell lung cancer; OS: overall survival; ROC: Receiver Operating Characteristics; TCGA: The Cancer Genome Atlas.