介绍：铜依赖性细胞死亡，即铜病死（cuproptosis），包括铜结合到硫代乙酰三羧酸（TCA）循环组分的脂酰基化过程。在铜病死中，铁硫蛋白1（FDX1）和脂酰化作为关键调节因子。铜病死的机制与目前对细胞死亡的认知不同，可能会促进铜在癌症治疗中的研究。脑胶质瘤是最常见的原发性颅内肿瘤，其预后极其不良。在脑胶质瘤患者中，手术和化疗等常规治疗效果有限。多种细胞死亡方式已被证实在脑胶质瘤肿瘤发生和进展中起作用，并参与肿瘤微环境（TME）。本研究旨在探讨铜病死是否影响脑胶质瘤肿瘤发生。 方法：通过比较癌症基因组图谱数据库（The Cancer Genome Atlas, TCGA）中脑胶质瘤组织及其相邻组织的基因表达谱来全面评估与铜病死相关的基因表达谱。从TCGA和基因表达 Omnibus（GEO）数据库检索获得的低级别脑胶质瘤（LGG）和胶质母细胞瘤的基因表达、预后、临床和病理数据。数据集由"Combat"算法管理以消除批处理效应，然后进行整合。基于分离中心点（PAM）算法的一致性聚类算法用于将725名LGG和胶质母细胞瘤患者分成两种铜病死亚型。根据两种铜病死亚型中差异表达的基因，将725名患者分为两种基因亚型。此外，建立与TME相关的评分系统以预测患者的生存和免疫治疗效果。此外，我们还构建了一个预后CRG得分和图谱系统，以预测脑胶质瘤患者的预后。收集83例脑胶质瘤患者的95个组织标本，包括相邻组织。使用免疫组化和RT-qPCR验证了这些临床样本中与铜病死相关的基因表达和CRG得分预测能力。 结果：我们的研究揭示了铜病死相关基因在细胞周期、TME、临床病理特征和预后中的广泛调控机制。我们还基于铜病死开发了一个预后模型。通过数据库和临床样本的验证，我们认为铜病死影响脑胶质瘤的预后，并可能为新的脑胶质瘤研究提供新途径。 结论：我们建议铜病死在治疗脑胶质瘤方面具有潜在重要性，并可用于新的脑胶质瘤研究。版权所有 © 2023 Wu、Li、Zhu、Li、Zhou、Chen、Huang、Zhang、Jiang和Ren。

Introduction: A copper-dependent cell death, cuproptosis, involves copper binding with lipoylated tricarboxylic acid (TCA) cycle components. In cuproptosis, ferredoxin 1 (FDX1) and lipoylation act as key regulators. The mechanism of cuproptosis differs from the current knowledge of cell death, which may invigorate investigations into copper's potential as a cancer treatment. An extremely dismal prognosis is associated with gliomas, the most prevalent primary intracranial tumor. In patients with glioma, conventional therapies, such as surgery and chemotherapy, have shown limited improvement. A variety of cell death modes have been confirmed to be operative in glioma oncogenesis and participate in the tumor microenvironment (TME), implicated in glioma development and progression. In this study, we aimed to explore whether cuproptosis influences glioma oncogenesis. Methods: Gene expression profiles related to cuproptosis were comprehensively evaluated by comparing adjacent tissues from glioma tissues in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) database. Gene expression, prognostic, clinical, and pathological data of lower-grade gliomas (LGG) and glioblastoma were retrieved from TCGA and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. The datasets were managed by "Combat" algorithm to eliminate batch effects and then combined. A consensus clustering algorithm based on the Partitioning Around Medoid (PAM) algorithm was used to classified 725 patients with LGG and glioblastoma multiforme (GBM) into two cuproptosis subtypes. According to the differentially expressed genes in the two cuproptosis subtypes, 725 patients were divided into 2 gene subtypes. Additionally, a scoring system that associated with TME was constructed to predict patient survival and patient immunotherapy outcomes. Furthermore, we constructed a prognostic CRG-score and nomogram system to predict the prognosis of glioma patients. 95 tissue specimens from 83 glioma patients undergoing surgical treatment were collected, including adjacent tissues. Using immunohistochemistry and RT-qPCR, we verified cuproptosis-related genes expression and CRG-score predictive ability in these clinical samples. Results: Our results revealed extensive regulatory mechanisms of cuproptosis-related genes in the cell cycle, TME, clinicopathological characteristics, and prognosis of glioma. We also developed a prognostic model based on cuproptosis. Through the verifications of database and clinical samples, we believe that cuproptosis affects the prognosis of glioma and potentially provides novel glioma research approaches. Conclusion: We suggest that cuproptosis has potential importance in treating gliomas and could be utilized in new glioma research efforts.Copyright © 2023 Wu, Li, Zhu, Li, Zhou, Chen, Huang, Zhang, Jiang and Ren.