Subarachnoid hemorrhage (SAH) is a cerebrovascular accident with an acute onset, severe disease characteristics, and poor prognosis. Within 72 hours after the occurrence of SAH, a sequence of pathological changes occur in the body including blood-brain barrier breakdown, cerebral edema, and reduced cerebrovascular flow that are defined as early brain injury (EBI), and it has been demonstrated that EBI exhibits an obvious correlation with poor prognosis. Ferroptosis is a novel programmed cell death mode. Ferroptosis is induced by the iron-dependent accumulation of lipid peroxides and reactive oxygen species (ROS). Ferroptosis involves abnormal iron metabolism, glutathione depletion, and lipid peroxidation. Recent study revealed that ferroptosis is involved in EBI and is significantly correlated with poor prognosis. With the gradual realization of the importance of ferroptosis, an increasing number of studies have been conducted to examine this process. This review summarizes the latest work in this field and tracks current research progress. We focused on iron metabolism, lipid metabolism, reduction systems centered on the GSH/GPX4 system, other newly discovered GSH/GPX4-independent antioxidant systems, and their related targets in the context of early brain injury. Additionally, we examined certain ferroptosis regulatory mechanisms that have been studied in other fields but not in SAH. A link between death and oxidative stress has been described. Additionally, we highlight the future research direction of ferroptosis in EBI of SAH, and this provides new ideas for follow-up research.

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.