Research Paper Volume 12, Issue 2 pp 1366—1376
Development of prognostic index based on autophagy-related genes analysis in breast cancer
- 1 Department 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
- 2 Department of Breast, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou 510220, Guangdong, China
- 3 Department of Radiotherapy, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou 510220, Guangdong, China
Received: October 14, 2019 Accepted: December 25, 2019 Published: January 22, 2020
https://doi.org/10.18632/aging.102687How to Cite
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.