Comprehensive Characterization of Pyroptosis Patterns with Implications in Prognosis and Immunotherapy in Low-Grade Gliomas

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.