Jin Ji, Li Ren, Guo Geng, Chen Yang, Li Zi-Ao, Zheng Jianzhong
School of Public Health, Shanxi Medical University, Taiyuan, 030001, China.
Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
J Environ Pathol Toxicol Oncol. 2023;42(3):53-70. doi: 10.1615/JEnvironPatholToxicolOncol.2023047159.
Glioma is the most common tumor of the central nervous system (CNS). Drug resistance, and lack of effective treatment methods make the treatment effect of glioma patients unsatisfactory. The recent discovery of cuproptosis has led to new thinking about the therapeutic and prognostic targets of glioma. The transcripts and clinical data of glioma samples were obtained from The cancer genome atlas (TCGA). The cuproptosis-related lncRNA (CRL)-based glioma prognostic models were built through least absolute shrinkage and selection operator (LASSO) regression analysis in the train set and validated in the test set. Kaplan-Meier survival curve, risk curve analysis, and time-dependent receiver operating characteristic (ROC) curve were used to assess the predictive ability and risk differentiation ability of the models. Univariate and multivariate COX regression analyses were conducted on the models and various clinical features, and then nomograms were constructed to verify their predictive efficacy and accuracy. Finally, we explored potential associations of the models with immune function, drug sensitivity, and the tumor mutational burden of glioma. Four CRLs were selected from the training set of 255 LGG samples and the other four CRLs were selected from the training set of 79 GBM samples to construct the models. Follow-up analysis showed that the models have commendable prognostic value and accuracy for glioma. Notably, the models were also associated with the immune function, drug sensitivity, and tumor mutational burden of gliomas. Our study showed that CRLs were prognostic biomarkers of glioma, closely related to glioma immune function. CRLs may affect uniquely the sensitivity of glioma treatment. It will be a potential therapeutic target for glioma. CRLs will offer new perspectives on the prognosis and therapy of gliomas.
胶质瘤是中枢神经系统（CNS）最常见的肿瘤。耐药性以及缺乏有效的治疗方法使得胶质瘤患者的治疗效果不尽人意。最近铜死亡的发现为胶质瘤的治疗和预后靶点带来了新的思路。胶质瘤样本的转录本和临床数据来自癌症基因组图谱（TCGA）。基于铜死亡相关lncRNA（CRL）的胶质瘤预后模型通过最小绝对收缩和选择算子（LASSO）回归分析在训练集中构建，并在测试集中进行验证。采用Kaplan-Meier生存曲线、风险曲线分析和时间依赖性受试者工作特征（ROC）曲线来评估模型的预测能力和风险区分能力。对模型和各种临床特征进行单变量和多变量COX回归分析，然后构建列线图以验证其预测效能和准确性。最后，我们探讨了模型与胶质瘤免疫功能、药物敏感性和肿瘤突变负荷之间的潜在关联。从255个低级别胶质瘤（LGG）样本的训练集中选择了4个CRL，从79个胶质母细胞瘤（GBM）样本的训练集中选择了另外4个CRL来构建模型。随访分析表明，这些模型对胶质瘤具有值得称赞的预后价值和准确性。值得注意 的是，这些模型还与胶质瘤的免疫功能、药物敏感性和肿瘤突变负荷相关。我们的研究表明，CRL是胶质瘤的预后生物标志物，与胶质瘤免疫功能密切相关。CRL可能独特地影响胶质瘤治疗的敏感性。它将成为胶质瘤潜在的治疗靶点。CRL将为胶质瘤的预后和治疗提供新的视角。
