A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques - Wang, Zitao; Bao, Anyu; Liu, Shiyi; Dai, Fangfang; Gong, Yiping; Cheng, Yanxiang (Frontiers in Genetics, 2022)
Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women’s health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironment in BC are limited. Therefore, our study aimed to investigate the potential prognostic value of pyroptosis-related genes (PRGs) and their relationship to immune microenvironment in BC. First, we identified that 38 differentially expressed PRGs between BC and normal tissues. Further on, the least absolute shrinkage and selection operator (LASSO) Cox regression and computational biology techniques were applied to construct a four-gene signature based on PRGs and patients in The Cancer Genome Atlas (TCGA) cohort was classified into high and low risk groups. Patients in the high-risk group showed significantly lower survival possibilities compared with the high-risk group, which was also verified in an external cohort. Furthermore, the risk model was characterized as an independent factor for predicting the overall survival (OS) of BC patients. What’s more important, functional enrichment analyses demonstrated the robust correlation between risk score and immune infiltration, thereby we summarized genetic mutation variation of PRGs, evaluated the relationship between PRGs, different risk group and immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint blockers (ICB), which indicated that low-risk group was enriched in higher TMB, more abundant immune cells, subsequently brighter prognosis. Except for that, the lower expression of PRGs such as GZMB, IL18, IRF1 and GZMA represented better survival, which verified the association between pyroptosis and immune landscape. In conclusion, we performed a comprehensive bioinformatics analysis and established a four-PRGs signature consisting of GZMB, IL18, IRF1 and GZMA, which could robustly predict the prognosis of BC patients.