Identification of a pyroptosis-related prognostic signature in breast cancer

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.

The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer

“…We also listed the other signatures that focus more on short-term survival, such as 1-year, 2-year, 3-year, and 5-year survival. We found our signature have similar short-term survival (3-year) prognostic value compared with them, such as the ferroptosis-related gene signature ( Wang et al, 2021a ; Wu et al, 2021a ; Zhu et al, 2021b ), pyroptosis-related gene signature ( Chen et al, 2022 ), necroptosis-related gene signature ( Yu et al, 2022 ), apoptosis-related gene signature ( Zou et al, 2022 ), zinc finger protein-related gene signature ( Ye et al, 2021 ), autophagy-related gene signature ( Lin et al, 2020 ), and metabolic-related gene signatures ( Lu et al, 2022b ). In addition, our model only involves four genes, while other models (11/13) tend to have more, which is more convenient to use to a certain extent.…”

“…To further explore the predictive ability of our signature, a comparison was performed among several significant molecular signatures employed for predicting OS in patients with BC. Compared with the other signature, such as ferroptosis, necroptosis, pyroptosis, and immune-related ( Lin et al, 2020 ; Zhong et al, 2020 ; Wang et al, 2021a ; Wu et al, 2021a ; Zhu et al, 2021b ; Ding et al, 2021 ; Ye et al, 2021 ; Lu et al, 2022a ; Lu et al, 2022b ; Chen et al, 2022 ; Chu et al, 2022 ; Yu et al, 2022 ; Zou et al, 2022 ), our signature indicated much higher AUCs, which indicated a better predictive ability, especially in predicting the long-term survival status. Furthermore, we constructed a prognostic nomogram that could simplify treatment decision-making for patients with BC.…”

Identification of differentially expressed genes at the single-cell level and prognosis prediction through bulk RNA sequencing data in breast cancer

“…Furthermore, tumor-antagonizing genes could advance our understanding of tumor escape from immune recognition and elimination (Bates et al, 2018). Increasing evidence from studies of bulk RNA-seq data have provided plenty of support for the search of new targets or new therapeutics (Liu et al, 2018;Chen et al, 2022). However, the DEGs from bulk RNA-seq could only represent the average expression levels of the genes.…”

“…The microarray (Abd-Elnaby et al, 2021) and next-generation sequencing (NGS) (Hong et al, 2020) technologies provide convenient approaches for gene expression profiling. These technologies have proven beneficial for risk prediction, patient stratification, and mechanism investigation (Chen et al, 2022).…”

“…A tumor microenvironment contains multiple components or various cells, such as cancer cells and immune cells (Li et al, 2007;Shi et al, 2020). Numerous bulk RNA-seq analyses provided hints about the differences between a normal and tumor microenvironment (Wang et al, 2020;Lu et al, 2021;Chen et al, 2022). However, bulk RNA-seq only provides average expression profiles which may lead to misunderstanding of the cardinal features.…”