Ji Xiangtian, Chen Xin, Lin Guozhong, Ma Kaiming, Yang Junhua, Zhao Xiaofang, Chen Suhua, Yang Jun
Department of Neurosurgery, Peking University Third Hospital, Beijing, China.
Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Beijing, China.
Front Cell Neurosci. 2024 Sep 18;18:1440409. doi: 10.3389/fncel.2024.1440409. eCollection 2024.
Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of in gliomas and explored its relationship with the cell cycle.
Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between expression and predicted treatment response across these databases.
expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more expression than the other subgroups. emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between expression and the sensitivity of multiple anti-cancer drugs.
These findings confirmed as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, may be used to predict glioma prognosis and has potential therapeutic value.
胶质瘤起源于大脑中最常见的非神经元细胞(神经胶质细胞),是最常见的脑肿瘤,与高死亡率和不良预后相关。胶质瘤细胞表现出破坏正常细胞周期调控的倾向,导致异常增殖和恶性生长。本研究调查了[具体基因名称未给出]在胶质瘤中的预测潜力,并探讨了其与细胞周期的关系。
使用中国胶质瘤基因组图谱(CGGA)和癌症基因组图谱(TCGA)数据库对RNA测序和单细胞测序数据进行回顾性分析。分析了[具体基因名称未给出]在具有各种病理特征的胶质瘤以及不同非神经元细胞组中的差异表达。使用基因集变异分析(GSVA)检查功能数据。此外,利用CellMiner评估这些数据库中[具体基因名称未给出]表达与预测治疗反应之间的关系。
[具体基因名称未给出]表达在高级别胶质瘤和1p/19q非缺失胶质瘤中富集。在TCGA胶质瘤亚型组的经典型和间充质型亚型中观察到[具体基因名称未给出]富集。在单细胞亚组分析中,与其他非神经元细胞相比,胶质瘤组织中[具体基因名称未给出]表达更高。此外,TCGA经典型胶质瘤细胞亚型的[具体基因名称未给出]表达高于其他亚组。[具体基因名称未给出]成为胶质瘤总体生存的独立预后因素。GSVA揭示了[具体基因名称未给出]可能影响胶质瘤细胞周期调控的潜在机制。最后,观察到[具体基因名称未给出]表达与多种抗癌药物的敏感性之间存在显著相关性。
这些发现证实[具体基因名称未给出]为一种新型生物标志物,并深入了解了非神经元细胞中的差异基因表达以及细胞周期对胶质瘤的影响。因此,[具体基因名称未给出]可用于预测胶质瘤预后并具有潜在治疗价值。