By estimation, gliomas account for approximately 80% of central nervous system malignant tumors worldwide [14]. Despite improvements in diagnosis and treatment in recent years, the prognosis of gliomas, especially glioblastomas, remains unsatisfactory.
Cell death is a necessary condition to maintain the growth and development of organisms [15]. One of the critical characteristics of tumor cells is it’s resistance to cell death. By resisting death and avoiding immune killing, tumor cells achieve continuous division and proliferation out of control of the normal growth regulatory system [4]. However, the hypermetabolism of tumor cells causes a relative shortage of oxygen and nutrients needed for tumor growth and induces necrotizing cell death in the interior of solid tumors [16]. With new discoveries of cell death pathways and related mechanisms, the understanding of the role of cell death in tumors is constantly deepening. The prognostic value of multiple DCD-related genes in malignant tumors, including glioma, has been consistently validated. For example, Wang et al. constructed a risk model based on cuproptosis-related lncRNAs, which showed good prognostic prediction performance and indicated the immuno‐microenvironment status for glioma [13]. Chen et al. identified 11 ferroptosis-related genes highly correlated with the prognosis of glioma patients[17]. However, there is a lack of a comprehensive evaluation of all DCD-related genes in terms of their prognostic value, strata performance and immunologic characteristics in glioma, which is the scientific problem our study aims to address.
Numerous studies have demonstrated that infiltrating immune cells in the microenvironment exert dual effects of promoting tumor and antitumor growth [18, 19]. On account of the destruction of blood‒brain barrier integrity by gliomas and the lymphatic outflow channels, the immune system can communicate with cells within the central nervous system [20, 21]. The immune infiltration of gliomas is characterized by extensive spatial and molecular heterogeneity. Gliomas can be infiltrated by a variety of immune cells, including macrophages, microglia, B cells, T cells, myeloid suppressor cells (MDSCs), etc.[22]. Previous studies have shown that the number of microglia and macrophages is positively correlated with glioma grade and invasiveness [23]. Our study revealed that both monocytes and M2 macrophages showed significantly higher infiltrating levels in Cluster 1 than in Cluster 2. Monocytes infiltrate the tumor and differentiate into tumor-associated macrophages and dendritic cells, which influence the tumor microenvironment through various mechanisms and result in immune tolerance, angiogenesis and metastasis [24]. M2 macrophages synthesize and release many anti-inflammatory factors (such as IL-10 and TGF-β). ), immunosuppressive factors and a variety of cytokines, inhibiting the inflammatory response and promoting tumor growth and metastasis [25]. In addition, another type of cancer-promoting immune cell, type-2 helper (Th2) cells, was upregulated in Cluster 1 compared with Cluster 2. Alternatively, some antitumor immune cells, such as plasma cells, M1 macrophages and activated mast cells, were upregulated in Cluster 2. The results showing more abundant M2 macrophages and Th2 cells but fewer M1 macrophages and plasma cells in Cluster 1 was consistent with the analysis that Cluster 1 showed a poorer prognosis than Cluster 2. For most HLA genes and immunological checkpoint genes (ICGs), their expression levels in Cluster 1 were higher than in Cluster 2. Therefore, these HLA genes and ICGs are expected to be potentially effective therapeutic targets for Cluster 1.
In most cancers, a higher TMB represents a better response to immune checkpoint suppression therapy. However, high TMB in gliomas has rarely been reported to be associated with better survival outcomes in response to immunotherapy. In a study published in 2021 [26], in two queues layered by TMB, patients with recurrent GBM (rGMB) with a TMB ≤ median lived longer after anti-PD-1/PD-L1 treatment than patients with a TMB > median. Therefore, TMB may not be an independent predictor of the response to immunotherapy in glioma. Instead, we used TIDE to predict the response to immunotherapy. The TIDE score provides a better assessment of the efficacy of anti-PD1 and anti-CTLA4 therapies than widely used biomarkers (TMB, PD-L1, and interferon-γ). In addition, TIDE was stable in predicting efficacy regardless of the tumor-infiltrating level of cytotoxic T cells [27]. Our analyses suggested that high risk tends to be associated with immunotherapy nonresponse compared to low risk.
It should be noted that since DCD contains a wide variety of cell death pathways, the distinct DCD levels may cause different effects, leading to dual roles in glioma. When the DCD level is not sufficient to induce cell death, glioma cells may activate intrinsic signaling pathways as response to stimulation and resist death. However, sufficient levels of DCD to induce cancer cell death may be a promising option for glioma therapies.
Nevertheless, there are still some limitations. First, phase 3 randomized controlled trials are lacking in this study, so the decision-making role and strata performance of this DCD-related model in a specific patient population cannot be verified. Second, the biological functions of some DCD genes need to be investigated intensively in both in vivo and in vitro experimental studies to fully understand their roles in the pathogenesis and progression of glioma.
In conclusion, we established a DCD-related signature classification model and a DCD-based risk model to predict the prognosis and intratumor microenvironment for glioma patients, which is of significance for the development of glioma treatment strategies.