Department of Neurosurgery, The Second Affiliated Hospital of Hengyang Medical College, Hengyang, 421000 Hunan, China.
Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, 130000 Jilin, China.
Mediators Inflamm. 2023 Feb 13;2023:6232620. doi: 10.1155/2023/6232620. eCollection 2023.
Patients with low-grade glioma (LGG) have wildly varying average lifespans. However, no effective way exists for identifying LGG patients at high risk. Cuproptosis is a recently described form of cell death associated with the abnormal aggregation of lipid acylated proteins. Few investigations have been conducted on cuproptosis-associated genes and LGG thus far. The purpose of this research is to establish a predictive model for cuproptosis-related genes in order to recognise LGG populations at high risk.
We analyzed 926 LGGs from 2 public datasets, all of which were RNA sequencing datasets. On the basis of immune scores, the LGG population was split into different risk categories with X-tile. LASSO and Cox regressions were employed to filter cuproptosis-associated genes and construct prediction models. The accuracy of the predictive models was measured by using TCGA internal validation set and the CGGA external validation set. In addition, LGG immune cell infiltration was viewed using CIBERSORT and ssGSEA algorithms and correlation analysis was done with cuproptosis-related genes. Finally, immune escape capacity in LGG low- and high-risk groups was evaluated using the TIDE method.
The prediction model constructed by four cuproptosis-related genes was used to identify high-risk populations in LGG. It performed well in training and all validation sets (AUC values: 0.915, 0.894, and 0.774). Meanwhile, we found that FDX1 and ATP7A in the four cuproptosis-related genes were positively correlated with immune response, while GCSH and ATP7B were opposite. In addition, the high immune score group had a lower TIDE score, indicating that their immune escape capacity was weak.
High-risk individuals in LGG can be reliably identified by the model based on cuproptosis-related genes. Furthermore, cuproptosis is closely related to tumor immune microenvironment, which gives a novel approach to treating LGG.
低级别胶质瘤（LGG）患者的平均寿命差异很大。但是，目前尚无有效的方法可以识别高危 LGG 患者。铜死亡是一种最近描述的细胞死亡形式，与脂质酰化蛋白的异常聚集有关。迄今为止，对铜死亡相关基因与 LGG 的研究很少。本研究旨在建立预测铜死亡相关基因的模型，以识别高危 LGG 人群。
我们分析了来自 2 个公共数据集的 926 个 LGG 样本，这些样本均为 RNA 测序数据集。根据免疫评分，使用 X-tile 将 LGG 人群分为不同的风险类别。使用 LASSO 和 Cox 回归筛选铜死亡相关基因并构建预测模型。使用 TCGA 内部验证集和 CGGA 外部验证集来评估预测模型的准确性。此外，使用 CIBERSORT 和 ssGSEA 算法观察 LGG 免疫细胞浸润，并与铜死亡相关基因进行相关性分析。最后，使用 TIDE 方法评估 LGG 低风险和高风险组的免疫逃逸能力。
使用四个铜死亡相关基因构建的预测模型用于识别 LGG 中的高危人群。该模型在训练集和所有验证集（AUC 值分别为 0.915、0.894 和 0.774）中表现良好。同时，我们发现四个铜死亡相关基因中的 FDX1 和 ATP7A 与免疫反应呈正相关，而 GCSH 和 ATP7B 则相反。此外，高免疫评分组的 TIDE 评分较低，表明其免疫逃逸能力较弱。
基于铜死亡相关基因的模型可以可靠地识别 LGG 中的高危个体。此外，铜死亡与肿瘤免疫微环境密切相关，为治疗 LGG 提供了新的方法。
