A pyroptosis-related gene signature for prognostic and immunological evaluation in breast cancer

Purpose Pyroptosis exerts an undesirable impact on the clinical outcome of breast cancer. Since any single gene is insufficient to be an appropriate marker for pyroptosis, our aim is to develop a pyroptosis-related gene (PRG) signature to predict the survival status and immunological landscape for breast cancer patients. Methods The information of breast cancer patients was retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the gene expressions of this signature in breast cancer. Its prognostic value was evaluated by univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, receiver operating characteristics (ROCs), univariate/multivariate analysis, and nomogram. Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to explore its potential biological function in breast cancer. The potential correlation between this signature and tumor immunity was revealed based on single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms. Results A PRG signature containing GSDMC, GZMB, IL18, and TP63 was created in a TCGA training cohort and validated in two validation GEO cohorts GSE58812 and GSE37751. Compared with a human mammary epithelial cell line MCF-10A, the expression levels of GSDMC, GZMB and IL18 were upregulated, while TP63 was found with lower expression level in breast cancer cells SK-BR-3, BT-549, MCF-7, and MDA-MB-231 using RT-qPCR assay. Based on univariate and multivariate Cox models, ROC curve, nomogram as well as calibration curve, it was revealed that this signature with high-risk score could independently predict poor clinical outcomes in breast cancer. Enrichment analyses demonstrated that the involved mechanism was tightly linked to immune-related processes. SsGSEA, ESTIMATE and CIBERSORT algorithms further pointed out that the established model might exert an impact on immune cell abundance, immune cell types and immune-checkpoint markers. Furthermore, individuals with breast cancer responded differently to these therapeutic agents based on this signature. Conclusions Our data suggested that this PRG signature with high risk was tightly associated with impaired immune function, possibly resulting in an unfavorable outcome for breast cancer patients.