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

• Published in: Aging (Albany, NY.) Vol. 12; no. 2; pp. 1366 - 1376
• Main Authors: Lin, Qing-Guang, Liu, Wei, Mo, Yu-Zhen, Han, Jing, Guo, Zhi-Xing, Zheng, Wei, Wang, Jian-Wei, Zou, Xue-Bin, Li, An-Hua, Han, Feng
• Format: Journal Article
• Language: English
• Published: United States Impact Journals 22.01.2020
• Subjects: Autophagy - genetics; Autophagy-Related Proteins - genetics; Biomarkers, Tumor; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Computational Biology - methods; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Kaplan-Meier Estimate; Mutation; Neoplasm Grading; Neoplasm Staging; Prognosis; Research Paper; ROC Curve; breast cancer; prognosis; autophagy-related genes
• ISSN: 1945-4589; 1945-4589
• DOI: 10.18632/aging.102687

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