Identification of senescence-related subtypes, establishment of a prognosis model, and characterization of a tumor microenvironment infiltration in breast cancer

Breast cancer is a malignancy with the highest incidence and mortality in women worldwide. Senescence is a model of arrest in the cell cycle, which plays an important role in tumor progression, while the prognostic value of cellular senescence-related genes (SRGs) in evaluating immune infiltration and clinical outcomes of breast cancer needs further investigation. In the present study, we identified two distinct molecular subtypes according to the expression profiles of 278 SRGs. We further explored the dysregulated pathways between the two subtypes and constructed a microenvironmental landscape of breast cancer. Subsequently, we established a senescence-related scoring signature based on the expression of four SRGs in the training set (GSE21653) and validated its accuracy in two validation sets (GSE20685 and GSE25055). In the training set, patients in the high-risk group had a worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed that risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The prognostic value of this signature was further confirmed in the validation sets. We also observed that a lower risk score was associated with a higher pathological response rate in patients with neoadjuvant chemotherapy. We next performed functional experiments to validate the results above. Our study demonstrated that these cellular senescence patterns effectively grouped patients at low or high risk of disease recurrence and revealed their potential roles in the tumor-immune-stromal microenvironment. These findings enhanced our understanding of the tumor immune microenvironment and provided new insights for improving the prognosis of breast cancer patients.

Automated summary

This study identified different molecular subtypes of breast cancer based on the expression profiles of cellular senescence-related genes (SRGs) and established a senescence-related scoring signature that can predict the prognosis of breast cancer patients. The accuracy of this signature was validated and it was found to be associated with pathological response rate. The study also revealed the potential roles of these senescence patterns in the tumor-immune-stromal microenvironment.