Weifang Medical University, Weifang, Shandong, China.
Brain Intensive Care Unit, Sunshine Union Hospital, Weifang, Shandong, China.
Folia Neuropathol. 2024;62(1):59-75. doi: 10.5114/fn.2023.132980.
This research hoped to explore the molecular mechanism of neutrophil extracellular traps (NETs) on glioblastoma multiforme (GBM) progression, and develop a promising prognostic signature for GBM based on NETs-related genes (NETGs).
Gene expression data and clinical data of GBM tumour samples were downloaded from TCGA and CGGA databases. NETs-related molecular subtypes were explored by using ConsensusClusterPlus. The NETGs with a prognostic value were identified, and then a prognostic model was constructed using LASSO Cox regression. The predicted performance of the prognostic model was evaluated using TCGA training and CGGA validation cohorts. Moreover, independent prognostic indicators were identified by univariate and multivariate analysis to generate the nomogram model. The sensitivities for antitumor drugs and immunotherapy were predicted. Finally, hub genes in the prognostic model were validated using qPCR analysis.
GBM patients were divided into two molecular subtypes with significant differences in tumour microenvironment (TME) score, survival, and immune infiltration. A NETGs signature was constructed based on seven genes (CPPED1, F3, G0S2, MME, MMP9, MAPK1, and MPO), which had a high value for predicting prognosis. A nomogram was constructed by two independent prognostic factors (age and risk score), which could be used to predict 1-, 2-, and 3-year survival probability of GBM. Patients in the high-risk group were more sensitive to bicalutamide, gefitinib, and dasatinib; patients in the low-risk group were associated with poor response to immunotherapy. The validation of the six genes in the prognostic model was consistent with the results of bioinformatics analysis.
The NETs-based prognostic model and nomogram proposed in this study are promising prognostic prediction tools for GBM, which may provide new ideas for the development of precise tumour targeted therapy.
本研究旨在探索中性粒细胞胞外诱捕网（NETs）对胶质母细胞瘤（GBM）进展的分子机制，并基于 NETs 相关基因（NETGs）开发一个有前景的 GBM 预后标志物。
从 TCGA 和 CGGA 数据库下载 GBM 肿瘤样本的基因表达数据和临床数据。使用 ConsensusClusterPlus 探索 NETs 相关分子亚型。确定具有预后价值的 NETGs，然后使用 LASSO Cox 回归构建预后模型。使用 TCGA 训练和 CGGA 验证队列评估预后模型的预测性能。此外，通过单变量和多变量分析确定独立的预后指标，以生成列线图模型。预测抗肿瘤药物和免疫治疗的敏感性。最后，使用 qPCR 分析验证预后模型中的关键基因。
GBM 患者被分为两个分子亚型，在肿瘤微环境（TME）评分、生存和免疫浸润方面存在显著差异。基于七个基因（CPPED1、F3、G0S2、MME、MMP9、MAPK1 和 MPO）构建了 NETGs 特征，该特征对预测预后具有较高的价值。通过两个独立的预后因素（年龄和风险评分）构建了列线图，可以预测 GBM 的 1、2 和 3 年生存率。高风险组的患者对比卡鲁胺、吉非替尼和达沙替尼更敏感；低风险组的患者对免疫治疗反应较差。预后模型中六个基因的验证与生物信息学分析结果一致。
本研究提出的基于 NETs 的预后模型和列线图是有前途的 GBM 预后预测工具，可能为肿瘤精准靶向治疗的发展提供新的思路。
