A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation

Abstract

Abstract Background Pyroptosis is a newly discovered form of cell programmed necrosis, but its role and mechanism in cancer cells remain unclear. The aim of this study is to systematically analyze the transcriptional sequencing data of breast cancer (BC) to find a pyroptosis-related prognostic marker to predict the survival of BC patients. Methods The original RNA sequencing (RNA-seq) expression data and corresponding clinical data of BC were downloaded from The Cancer Genome Atlas (TGCA) database, followed by differential analysis. The pyroptosis-related differentially expressed genes (DE-PRGs) were employed to perform a computational difference algorithm and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was utilized to avoid overfitting. A total of 4 pyroptosis-related genes (PRGs) with potential prognostic value were identified, and a risk scoring formula was constructed based on these genes. According to the risk scores, the patients could be classified into high- and low-risk score groups. The potential molecular mechanisms and properties of PRGs were explored by computational biology and verified in Gene Expression Omnibus (GEO) datasets. In addition, the quantitative real time PCR (RT-qPCR) and Human Protein Atlas (HPA) were performed to validate the expression of the key genes. Results A PRGs signature, which was an independent prognostic factor, was constructed, and could divide patients into high- and low-risk groups. The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group both in TCGA and in GEO, indicating that the signature is valuable for survival prediction and personalized immunotherapy of BC patients. Conclusions The pyroptosis-related biomarkers were identified for BC prognosis. The findings of this study provide new insights into the development of the efficacy of personalized immunotherapy and accurate cancer treatment options.