Diverse cell death signature based subtypes predict the prognosis and immune characteristics within glioma

Background. Cell death plays an essential role in the pathogenesis, progression, drug resistance and recurrence of glioma. Although multiple cell death pathways are involved in glioma development, there is lack of a stratification and prognostic modelling for glioma based on the integration of diverse genes for cell deaths. Methods. In this study, 1254 diverse cell death (DCD)-related genes were assessed using the ConsensusClusterPlus assessment to identify DCD patterns in glioma. CIBERSORT, ssGSEA, and ESTIMATE algorithms were applied to evaluate immune microenvironment differences between subtypes. LASSO Cox regression was used to screen prognosis-related DCD genes, and a risk score model was constructed. TMB, TIDE, immune infiltration, and immunotherapy response was analyzed to evaluate the immune characteristics. Results. Two DCD-related subgroups named Clusters 1 and 2, with distinct DCD levels, immune characteristics, and prognoses, were determined from glioma samples. A DCD-based risk score model was developed to assess DCD levels in glioma patients and divide patients into high- and low-risk groups. We found this risk model can be used as an independent prognostic factor for glioma patients. Notably, glioma patients with low risk scores exhibited subdued DCD activity, prolonged survival, and a favorable disposition towards benefiting from immune checkpoint blockade therapies. Conclusions. This study established a novel signature classification and a risk model by comprehensively analyzing patterns of various DCDs to stratify glioma patients and to predict the prognosis and immune characteristics of glioma. We provided a theoretical basis for the clinical application of DCD-related genes in glioma prognosis and immunotherapy. Background. Cell death plays an essential role in the pathogenesis, progression, drug resistance and recurrence of glioma. Although multiple cell death pathways are involved in glioma development, there is lack of a stratification and prognostic modelling for glioma based on the integration of diverse genes for cell deaths. Methods. In this study, 1254 diverse cell death (DCD)-related genes were assessed using the ConsensusClusterPlus assessment to identify DCD patterns in glioma. CIBERSORT, ssGSEA, and ESTIMATE algorithms were applied to evaluate immune microenvironment differences between subtypes. LASSO Cox regression was used to screen prognosis-related DCD genes, and a risk score model was constructed. TMB, TIDE, immune infiltration, and immunotherapy response was analyzed to evaluate the immune characteristics. Results. Two DCD-related subgroups named Clusters 1 and 2, with distinct DCD levels, immune characteristics, and prognoses, were determined from glioma samples. A DCD-based risk score model was developed to assess DCD levels in glioma patients and divide patients into high- and low-risk groups. We found this risk model can be used as an independent prognostic factor for glioma patients. Notably, glioma patients with low risk scores exhibited subdued DCD activity, prolonged survival, and a favorable disposition towards benefiting from immune checkpoint blockade therapies. Conclusions. This study established a novel signature classification and a risk model by comprehensively analyzing patterns of various DCDs to stratify glioma patients and to predict the prognosis and immune characteristics of glioma. We provided a theoretical basis for the clinical application of DCD-related genes in glioma prognosis and immunotherapy. ### Competing Interest Statement The authors have declared no competing interest.