BACKGROUND: Cell death modulates the biological behaviors of tumors. However, the comprehensive role of the multiple forms of cell death in lower-grade glioma (LGG) is unknown. METHODS: We collected the transcriptional data of LGG patients from public repositories to comprehensively examine six cell death patterns (autophagy, apoptosis, cuproptosis, necroptosis, ferroptosis, and pyroptosis) in LGG samples and systematically correlated these patterns with patient survival, underlying biological processes, and drug sensitivity using serial bioinformatics analysis, clinical sample validation and assays. RESULTS: We identified and independently validated three reproducible cell death-based clusters associated with distinct clinical outcomes and tumor microenvironment characteristics. The Tumor Immune Dysfunction and Exclusion algorithm was applied for predicting how these three clusters would respond to immune checkpoint blockade (ICB) therapy; we found potential resistance of cluster B to ICB therapy. We also performed drug screening to identify cluster-specific drugs. Furthermore, a scoring system, designated as the CDPM score, was developed to estimate the cell death patterns of patients with LGG; this system could predict both LGG patients' prognosis and immunotherapy efficacy. By performing multiplex immunofluorescence staining, we validated the correlations of GNAL expression with the molecular and clinical features of LGG in an independent LGG cohort. Finally, assays showed that GNAL promoted apoptosis and inhibited the proliferation of LGG cells. CONCLUSION: The new cell death-based subtype system indicates several features of LGG biology and reveals novel insights into the use of precision medicine for treating LGG. The CDPM score could be used to predict the immunotherapy response and prognosis of LGG patients; moreover, it could indicate a novel direction for improving LGG management.