Comprehensive Analyses Identify a Signature Based on Pyroptosis-Related Genes for Breast Cancer

Purpose/Objective(s)

Pyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BC) is still not clear. Herein, we explored a signature based on pyroptosis-related genes (PRGs) through comprehensive analyses for BC.

Materials/Methods

The prognostic signature containing six PRGs, which can divide BC patients into high and low-risk groups with different prognosis, was constructed and validated by two independent cohorts. By assessing immune status, we found that a total of 79 immune microenvironments differed between the two subgroups. 11 immune checkpoint genes (BTLA, TNFRSF9, ICOS, PDCD1, TIGIT, CTLA4, LAG3, CD274, TNFRSF4, HAVCR2, and SIRPA) showed disparate expression levels in the two groups. Besides, BRCA patients in high-risk group exhibited lower TIDE scores than those in low-risk group, indicating that BRCA patients with higher RSs were more sensitive to ICB therapy. GSEA revealed that the risk groups were associated with tumor-related and immune-associated pathways. Another mentionable result was that the univariate and multivariate Cox regression analysis demonstrated that the risk model was an independent prognostic factor for BC patients. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents.

Results

We found six prognostic pryroptosis-relate genes (PRGs) from public database for BC. The PRG-signature based on the six PRGs was developed. In addition, we identified differences in enrichment pathways, immune microenvironment, immune checkpoints, and sensitivity to several chemotherapeutic agents between risk groups.

Conclusion

Our identified and validated risk model based on six pyroptosis-related genes is an independent prognostic factor for BC patients. Through comprehensive analyses, the findings of our study uncovered the potential biomarkers and therapeutic target for the risk model based on PRGs.