Department of Anesthesiology, the Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Department of Neurosurgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Front Immunol. 2023 Feb 6;14:1082974. doi: 10.3389/fimmu.2023.1082974. eCollection 2023.
Cuproptosis, a newly reported type of programmed cell death, takes part in the regulation of tumor progression, treatment response, and prognosis. But the specific effect of cuproptosis-related genes (CRGs) on glioblastoma (GBM) is still unclear.
The transcriptome data and corresponding clinical data of GBM samples were downloaded from the TCGA and GEO databases. R software and R packages were used to perform statistical analysis, consensus cluster analysis, survival analysis, Cox regression analysis, Lasso regression analysis, and tumor microenvironment analysis. The mRNA and protein expression levels of model-related genes were detected by RT-qPCR and Western blot assays, respectively.
The expression profile of CRGs in 209 GBM samples from two separate datasets was obtained. Two cuproptosis subtypes, CRGcluster A and CRGcluster B, were identified by consensus cluster analysis. There were apparent differences in prognosis, tumor microenvironment, and immune checkpoint expression levels between the two subtypes, and there were 79 prognostic differentially expressed genes (DEGs). According to the prognostic DEGs, two gene subtypes, geneCluster A and geneCluster B, were identified, and a prognostic risk score model was constructed and validated. This model consists of five prognostic DEGs, including PDIA4, DUSP6, PTPRN, PILRB, and CBLN1. Ultimately, to improve the applicability of the model, a nomogram was established. Patients with GBM in the low-risk cluster have a higher mutation burden and predict a longer OS than in the high-risk group. Moreover, the risk score was related to drug sensitivity and negatively correlated with the CSC index.
We successfully constructed a cuproptosis-related prognostic model, which can independently predict the prognosis of GBM patients. These results further complement the understanding of cuproptosis and provide new theoretical support for developing a more effective treatment strategy.
铜死亡是一种新报道的程序性细胞死亡方式，参与肿瘤进展、治疗反应和预后的调节。但铜死亡相关基因（CRGs）对胶质母细胞瘤（GBM）的确切影响仍不清楚。
从 TCGA 和 GEO 数据库中下载 GBM 样本的转录组数据和相应的临床数据。使用 R 软件和 R 包进行统计分析、共识聚类分析、生存分析、Cox 回归分析、Lasso 回归分析和肿瘤微环境分析。通过 RT-qPCR 和 Western blot 检测模型相关基因的 mRNA 和蛋白表达水平。
从两个独立数据集的 209 个 GBM 样本中获得了 CRGs 的表达谱。通过共识聚类分析鉴定出两个铜死亡亚型，CRGcluster A 和 CRGcluster B。这两个亚型在预后、肿瘤微环境和免疫检查点表达水平上存在明显差异，并且存在 79 个预后差异表达基因（DEGs）。根据预后 DEGs，鉴定出两个基因亚型，基因Cluster A 和基因Cluster B，并构建和验证了一个预后风险评分模型。该模型由 5 个预后 DEGs 组成，包括 PDIA4、DUSP6、PTPRN、PILRB 和 CBLN1。最终，为了提高模型的适用性，建立了一个列线图。GBM 患者中低风险组的突变负担较高，OS 预测值高于高风险组。此外，风险评分与药物敏感性相关，与 CSC 指数呈负相关。
我们成功构建了一个铜死亡相关的预后模型，该模型可以独立预测 GBM 患者的预后。这些结果进一步补充了对铜死亡的理解，并为开发更有效的治疗策略提供了新的理论支持。
