https://orcid.org/0000-0002-7887-5232
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
A six ARG-based signature, including CLU, DGAT1, MXI1, NFKBI, PIK3CA and PLAU, 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.
Data Sharing Statement
The datasets used and/or analyzed during current study are available from the corresponding author on reasonable request.
Ethics Approval and Consent to Participate
This study was exempt from approval by the Ethics Committee of Sun Yat-sen University Cancer Center, because all data analyzed in the current study were downloaded from public databases of the TCGA (https://tcga-data.nci.nih.gov/tcga/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). We just reviewed gene expression files and corresponding clinicopathological information of patients without impairing their health and privacy disclosure.
Author Contributions
JY, FFD, WYZ, CGS, LW, WX, XH, ZYY, XWB and JJH contributed to conception, study design, execution, data acquisition, analysis, interpretation, and have written and substantially revised this manuscript. All authors reviewed and agreed on the final version of this manuscript, took responsibility and were accountable for the contents of this article.
Disclosure
The authors declare no conflicts of interest.