Identification of a pyroptosis-related prognostic signature in breast cancer

• Published in: BMC cancer Vol. 22; no. 1; pp. 429 - 16
• Main Authors: Chen, Hanghang, Luo, Haihua, Wang, Jieyan, Li, Jinming, Jiang, Yong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.04.2022; BioMed Central; BMC
• Subjects: Analysis; Atrophy; Biomarkers, Tumor - genetics; Breast Cancer; Breast Neoplasms - genetics; Cancer; Cancer therapies; Care and treatment; Cell death; Chemotherapy; Diagnosis; Female; Females; Gene expression; Gene set enrichment analysis; Genes; Genetic aspects; Genomes; Health aspects; Humans; Medical prognosis; Metastases; Microenvironments; Performance evaluation; Prognosis; Pyroptosis; Pyroptosis - genetics; Risk factors; Risk groups; RNA sequencing; Tumor immune microenvironment; Tumor Microenvironment - genetics; Tumor mutational burden; Tumorigenesis; Tumors; Taiwan; Tumor mutational burden; Tumor immune microenvironment; Prognosis; Pyroptosis; Breast Cancer
• DOI: 10.1186/s12885-022-09526-z
• ISSN: 1471-2407

The relationship between pyroptosis and cancer is complex. It is controversial that whether pyroptosis represses or promotes tumor development. This study aimed to explore prognostic molecular characteristics to predict the prognosis of breast cancer (BRCA) based on a comprehensive analysis of pyroptosis-related gene expression data. RNA-sequcing data of BRCA were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO) datasets. First, pyroptosis-related differentially expressed genes (DEGs) between normal and tumor tissues were identified from the TCGA database. Based on the DEGs, 1053 BRCA patients were divided into two clusters. Second, DEGs between the two clusters were used to construct a signature by a least absolute shrinkage and selection operator (LASSO) Cox regression model, and the GEO cohort was used to validate the signature. Various statistical methods were applied to assess this gene signature. Finally, Single-sample gene set enrichment analysis (ssGSEA) was employed to compare the enrichment scores of 16 types of immune cells and 13 immune-related pathways between the low- and high-risk groups. We calculated the tumor mutational burden (TMB) of TCGA cohort and evaluated the correlations between the TMB and riskscores of the TCGA cohort. We also compared the TMB between the low- and high-risk groups. A total of 39 pyroptosis-related DEGs were identified from the TCGA-breast cancer dataset. A prognostic signature comprising 16 genes in the two clusters of DEGs was developed to divide patients into high-risk and low-risk groups, and its prognostic performance was excellent in two independent patient cohorts. The high-risk group generally had lower levels of immune cell infiltration and lower activity of immune pathway activity than did the low-risk group, and different risk groups revealed different proportions of immune subtypes. The TMB is higher in high-risk group compared with low-risk group. OS of low-TMB group is better than that of high-TMB group. A 16-gene signature comprising pyroptosis-related genes was constructed to assess the prognosis of breast cancer patients and its prognostic performance was excellent in two independent patient cohorts. The signature was found closely associated with the tumor immune microenvironment and the potential correlation could provide some clues for further studies. The signature was also correlated with TMB and the mechanisms are still warranted.