An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer
Jing Yuan, Fangfang Duan, Wenyu Zhai, Chenge Song, Li Wang, Wen Xia, Xin Hua, Zhongyu Yuan, Xiwen Bi, Jiajia Huang
Abstract
BACKGROUND: Aging, an inevitable process characterized by functional decline over time, is a significant risk factor for various tumors. However, little is known about aging-related genes (ARGs) in breast cancer (BC). We aimed to explore the potential prognostic role of ARGs and to develop an ARG-based prognosis signature for BC. METHODS: RNA-sequencing expression profiles and corresponding clinicopathological data of female patients with BC were obtained from public databases in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). An ARG-based risk signature was constructed in the TCGA cohort based on results of least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, and its prognostic value was further validated in the GSE20685 cohort. RESULTS: , was developed in the TCGA cohort and significantly stratified patients into low- and high-risk groups. Patients in the former group showed significantly better prognosis than those in the latter. Multivariate Cox regression analysis demonstrated that the ARG risk score was an independent prognostic factor for BC. A predictive nomogram integrating the ARG risk score and three identified factors (age, N- and M-classification) was established in the TCGA cohort and validated in the GSE20685 cohort. Calibration plots showed good consistency between predicted survival probabilities and actual observations. CONCLUSION: A novel ARG-based risk signature was developed for patients with BC, which can be used for individual prognosis prediction and promoting personalized treatment.