Research Paper|Volume 12, Issue 2|pp 1366—1376

Development of prognostic index based on autophagy-related genes analysis in breast cancer

Qing-Guang Lin¹, Wei Liu², Yu-zhen Mo³, Jing Han¹, Zhi-Xing Guo¹, Wei Zheng¹, Jian-wei Wang¹, Xue-Bin Zou¹, An-Hua Li¹, Feng Han¹

¹Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong, China
²Department of Breast, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou 510220, Guangdong, China
³Department of Radiotherapy, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou 510220, Guangdong, China

* Co-first authors

Received: October 14, 2019Accepted: December 25, 2019Published: January 22, 2020

https://doi.org/10.18632/aging.102687

Copyright: © 2020 Lin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Autophagy is a self-digesting process that can satisfy the metabolic needs of cells, and is closely related to development of cancer. However, the effect of autophagy-related genes (ARGs) on the prognosis of breast cancer remains unclear.

Results: We first found that 27 ARGs were significantly associated with overall survival in breast cancer. The prognosis-related ARGs signature established using the Cox regression model consists of 12 ARGs that can be divided patients into high-risk and low-risk groups. The overall survival of patients with high-risk scores (HR 3.652, 2.410-5.533; P < 0.001) was shorter than patients with low-risk scores. The area under the receiver operating characteristic (ROC) curve for 1-year, 3-year, and 5-year survival rates were 0.739, 0.727, and 0.742, respectively.

Conclusion: The12-ARGs marker can predict the prognosis of breast cancer and thus help individualized treatment of patients at different risks.

Methods: Based on the TCGA dataset, we integrated the expression profiles of ARGs in 1,039 breast cancer patients. Differentially expressed ARGs and survival-related ARGs were evaluated by computational difference algorithm and COX regression analysis. In addition, we also explored the mutations in these ARGs. A new prognostic indicator based on ARGs was developed using multivariate COX analysis.