The Personnel Department, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Fengtai District, Beijing, China.
Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Medicine (Baltimore). 2023 Mar 3;102(9):e33150. doi: 10.1097/MD.0000000000033150.
Glioblastoma (GBM) is a highly malignant neurological tumor that has a poor prognosis. While pyroptosis affects cancer cell proliferation, invasion and migration, function of pyroptosis-related genes (PRGs) in GBM as well as the prognostic significance of PRGs remain obscure. By analyzing the mechanisms involved in the association between pyroptosis and GBM, our study hopes to provide new insights into the treatment of GBM. Here, 32 out of 52 PRGs were identified as the differentially expressed genes between GBM tumor versus normal tissues. And all GBM cases were assigned to 2 groups according to the expression of the differentially expressed genes using comprehensive bioinformatics analysis. The least absolute shrinkage and selection operator analysis led to the construction of a 9-gene signature, and the cancer genome atlas cohort of GBM patients were categorized into high risk and low risk subgroups. A significant increase in the survival possibility was found in low risk patients in comparison with the high risk ones. Consistently, low risk patients of a gene expression omnibus cohort displayed a markedly longer overall survival than the high risk counterparts. The risk score calculated using the gene signature was found to be an independent predictor of survival of GBM cases. Besides, we observed significant differences in the expression levels of immune checkpoints between the high risk versus low risk GBM cases, providing instructive suggestions for immunotherapy of GBM. Overall, the present study developed a new multigene signature for prognostic prediction of GBM.
胶质母细胞瘤(GBM)是一种高度恶性的神经肿瘤,预后不良。虽然细胞焦亡会影响癌细胞的增殖、侵袭和迁移,但细胞焦亡相关基因(PRGs)在 GBM 中的功能以及 PRGs 的预后意义尚不清楚。通过分析细胞焦亡与 GBM 之间的关联机制,我们的研究希望为 GBM 的治疗提供新的思路。在这里,在 GBM 肿瘤与正常组织之间的差异表达基因中,确定了 32 个 PRGs。并且使用综合生物信息学分析,根据差异表达基因的表达情况,将所有 GBM 病例分为 2 组。最小绝对收缩和选择算子分析导致构建了一个 9 基因特征,并且使用癌症基因组图谱队列的 GBM 患者被分为高风险和低风险亚组。与高风险组相比,低风险组的生存可能性显著增加。一致地,基因表达综合数据集队列的低风险患者的总生存期明显长于高风险患者。使用基因特征计算的风险评分被发现是 GBM 病例生存的独立预测因子。此外,我们观察到高风险与低风险 GBM 病例之间免疫检查点的表达水平存在显著差异,为 GBM 的免疫治疗提供了有指导意义的建议。总体而言,本研究开发了一种新的多基因特征,用于预测 GBM 的预后。