A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration

Abstract

Background Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. Methods In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. Results In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. Conclusion We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.

Bulk and Single-Cell Transcriptome Analyses Revealed That the Pyroptosis of Glioma-Associated Macrophages Participates in Tumor Progression and Immunosuppression

“…The AUC (area under the ROC curve) was 0.85, 0.90, and 0.87 for the 1-year, 3-year, and 5-year OS in the training cohort and 0.72, 0.80, and 0.84 in the CGGA cohort, respectively. The ROC curves showed a similar prognostic value of our established prognostic model and the previously established 10-pyroptosis-gene prognostic model [22] and golden standard WHO grading system for predicting OS at 1, 3, and 5 years in TCGA and CGGA cohorts (Figure S5). Moreover, univariate and multifactorial Cox regression analyses revealed that the Riskscore could be used as a valid independent prognostic factor, as well as disease grade, age, IDH mutation status, and 1p19q codeletion status (Figures 4(e) and 4(f)).…”

“…Although there have been several studies on prognosis-related pyroptosis genes in glioma [ 22 , 23 ], they have been limited to establishing a prognostic model based on regression analysis while ignoring the correlation between pyroptosis and the tumor immune microenvironment. Moreover, these studies have tended to analyze all pyroptosis-related genes in general, but different gasdermin-mediated pyroptosis pathways are relatively independent from each other, and many genes are involved in other biological functions, such as apoptosis, so each pathway should be explored independently to reflect the situation of pyroptosis in glioma more accurately.…”

Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

“…Furthermore, we compared the prognostic predictive abilities of 20 different risk signatures of gliomas in TCGA from published articles, including inflammatory response-related gene (IRRG) signature ( Yan et al, 2022 ), DNA damage and repair-related gene (DDRRG) signature ( Li et al, 2022c ), CXCR members signature ( He et al, 2022 ), pyroptosis-related gene signature ( Zhang M. et al, 2021b ; Chao et al, 2022 ; Yang et al, 2022 ; Zhang et al, 2022 ), ECM-related gene (ECMRG) signature ( Li et al, 2022b ), tripartite motif (TRIM) family gene signature ( Xiao et al, 2022 ), antigen presentation machinery (APM) signature ( Chen et al, 2022 ), natural killer cell-related gene (NKRG) signature ( Li C. et al, 2022a ), IL-4-related gene (IL4RG) signature ( Qi et al, 2022 ), hypoxia-related gene (HRG) signature ( Gao et al, 2021 ), S100 family-based signature ( Hu et al, 2021 ), TIME signature ( Zhang C. et al, 2021a ), focal adhesion-related gene (FARG) signature ( Li et al, 2021 ), m6A RNA methylation regulator signature ( Cong et al, 2021 ), HDAC1-related signature ( Fan et al, 2021 ), RNA-binding protein (RBP)-based signature ( Chen et al, 2021a ) and ferroptosis-related gene (FRG) signature ( Chen et al, 2021b ). The results of univariate and multivariate Cox analyses showed that our ARG signature had independent predictive ability ( p < 0.001, Table 1 ).…”

Comprehensive analysis of pyroptosis regulation patterns and their influence on tumor immune microenvironment and patient prognosis in glioma

“…However, the integrative roles of multiple pyroptosis-related genes are still poorly elucidated. By identifying the role of pyroptosis regulation patterns in TME, we may better understand the anti-tumor immune response and guide the design of more effective immunotherapy [ 38 ].…”