A risk model of 10 aging‐related genes for predicting survival and immune response in triple‐negative breast cancer. (16th March 2022)

• Record Type: Journal Article
• Authors: Yang, Xia; Sun, Yanhua; Liu, Xia; Jiang, Zhinong
• Is Part Of: Cancer medicine. Volume 11:Number 16(2022)
• Page Start: 3182
• Page End: 3193
• Publication Date: 2022-03-16
• Subjects: aging‐related genes -- immune response -- prognosis -- triple‐negative breast cancer; 616.994005
• DOI: 10.1002/cam4.4674
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Accumulated studies showed that the clinical significance of aging on the development and malignancy of tumors, while the relationship between aging and the prognosis, immune response in triple‐negative breast cancer (TNBC) has not been well clarified. Here, we constructed a risk model of 10 prognostic aging‐related genes (ARGs) from METABRIC database. Then, TNBC patients were classified into high‐ and low‐risk groups, the survival diversity, immune response, genomic function, and tumor mutation burden (TMB) between different risk groups were explored in METABRIC, TCGA, and GSE58812 cohorts. Results showed that patients in the high‐risk group had poorer survival outcomes compared to their counterparts (all p < 0.05), and the nomogram we established showed reliable prediction ability for survival in TNBC patients. Besides, TNBC patients with high‐risk scores had a lower expression of immune checkpoint markers and a lower fraction of activated immune cells. Furthermore, GSEA showed that Notch signaling pathway was significantly enriched in the high‐risk group. Thus, a risk model based on the aging‐related genes was developed and validated in this study, which may serve as a potential biomarker for prognosis and personalized treatment in TNBCs. Abstract : Using the mRNA profiles, clinical features and mutation data of TNBC from METABRIC, TCGA and GSE58812 databases, we identified a risk signature of 10 prognostic aging‐related genes (ARGs) to explore the survival diversity, immune status, genomic function, tumor mutation burden (TMB) and therapeutic response between different risk groups, which possibly have potential function to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.