Background: Due to high heterogeneity and mortality of low-grade gliomas (LGGs), it is of great significance to find biomarkers for prognosis and immunotherapy. Pyroptosis is emerging as an attractive target in cancer research for its effect on tumor immune microenvironment (TIME). However, the investigation of pyroptosis in LGGs is insufficient.Methods: LGG samples from TCGA and CGGA database were classified into two pyroptosis patterns based on the expression profiles of 52 PRGs using consensus clustering. A prognostic model was constructed by using the LASSO-COX method. ESTIMATE algorithm and single sample gene set enrichment analysis (ssGSEA) were used to characterize the TIME. Based on the differentially expressed genes between two pyroptosis patterns, favorable and unfavorable pyroptosis gene signatures were determined. Pyroptosis score scheme was constructed to quantify the pyroptosis patterns through gene set variation analysis (GSVA) method. Two external datasets and immunotherapy cohort from CGGA and GEO database were used to validate the predictive value of the pyroptosis score. The Human Protein Atlas website and Western blotting were utilized to confirm the expression of the selected genes in the prognostic model in LGGs.Results: Distinct overall survival and immune checkpoint blockage therapeutic responses were identified between two pyroptosis patterns. A low pyroptosis score in LGG patients implies higher overall survival, poor immune cell infiltration, and better response to immunotherapy of immune checkpoint blockage.Conclusion: Our findings provided a foundation for future research targeting pyroptosis and opened a new sight to explore the prognosis and immunotherapy from the angle of pyroptosis in LGGs.

Background:Due to the highly variable prognosis of low-grade gliomas(LGGs),it is important to find robust biomarkers for predicting clinical outcomes.Aging cancer-associated fibroblasts(CAFs)within the senescent stroma of a tumor microenvironment(TME)have been recently reported to play a key role in tumor development.However,there are few studies focusing on this topic in gliomas.Methods and Results:Based on the transcriptome data from TCGA and CGGA databases,we identified aging CAF-related genes(ACAFRGs)in LGGs by the weighted gene co-expression network analysis(WGCNA)method,followed by which LGG samples were classified into two aging CAF-related gene clusters with distinct prognosis and characteristics of the TME.Machine learning algorithms were used to screen out eight featured ACAFRGs to characterize two aging CAF-related gene clusters,and a nomogram model was constructed to predict the probability of gene cluster A for each LGG sample.Then,a powerful aging CAF scoring system was developed to predict the prognosis and response to immune checkpoint blockage therapy.Finally,the ACAFRGs were verified in two glioma-related external datasets.The performance of the aging CAF score in predicting the immunotherapy response was further validated in two independent cohorts.We also confirmed the expression of ACAFRGs at the protein level in glioma tissues through the Human Protein Atlas website and Western blotting analysis.Conclusion:We developed a robust aging CAF scoring system to predict the prognosis and immunotherapy response in LGGs.Our findings may provide new targets for therapeutics and contribute to the exploration focusing on aging CAFs.