Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer.

This study focused on screening novel markers associated with cellular senescence for predicting the prognosis of breast cancer. The RNA-seq expression profile of BRCA and clinical data were obtained from TCGA. The pam algorithm was used to cluster patients based on senescence-related genes. The weighted gene co-expression network analysis was used to identify co-expressed genes, and LASSO-Cox analysis was performed to build a risk prognosis model. The performance of the model was also evaluated. We additionally explored the role of senescence in cancer development and possible regulatory mechanism. The patients were clustered into 2 subtypes. A total of 5259 genes significantly related to senescence were identified by weighted gene co-expression network analysis. LASSO-Cox finally established a 6-signature risk model (ADAMTS8, DCAF12L2, PCDHA10, PGK1, SLC16A2, and TMEM233) that exhibited favorable and stable performance in our training, validation, and whole BRCA datasets. Furthermore, the superiority of our model was also observed after comparing it to other published models. The 6-signature was proved to be an independent risk factor for prognosis. In addition, mechanism prediction implied the activation of glycometabolism processes such as glycolysis and TCA cycle under the condition of senescence. Glycometabolism pathways were further found to negatively correlate with the infiltration level of CD8 T-cells and natural killer cells but positively correlate with M2 macrophage infiltration and expressions of tissue degeneration biomarkers, which suggested the deficit immune surveillance and risk of tumor migration. The constructed 6-gene model based on cellular senescence could be an effective indicator for predicting the prognosis of BRCA.