Yuan Qing, Zuo Fu-Xing, Cai Hong-Qing, Qian Hai-Peng, Wan Jing-Hai
Department of Neurosurgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Genet. 2022 Jul 8;13:912227. doi: 10.3389/fgene.2022.912227. eCollection 2022.
Studies have suggested that glioblastoma (GBM) cells originate from the subventricular zone (SVZ) and that GBM contact with the SVZ correlated with worse prognosis and higher recurrence. However, research on differentially expressed genes (DEGs) between GBM and the SVZ is lacking. We performed deep RNA sequencing on seven SVZ-involved GBMs and paired tumor-free SVZ tissues. DEGs and enrichment were assessed. We obtained GBM patient expression profiles and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the CGGA cohort. GBM patient data from TCGA cohort were used for validation. We identified 137 (97 up- and 40 down-regulated) DEGs between GBM and healthy SVZ samples. Enrichment analysis revealed that DEGs were mainly enriched in immune-related terms, including humoral immune response regulation, T cell differentiation, and response to tumor necrosis factor, and the MAPK, cAMP, PPAR, PI3K-Akt, and NF-κb signaling pathways. An eight-gene (, , , , , , , and ) signature was constructed. GBM patients were stratified into two risk groups. High-risk patients showed significantly reduced overall survival compared with low-risk patients. Univariate and multivariate regression analyses indicated that the risk score level represented an independent prognostic factor. High risk score of GBM patients negatively correlated with 1p19q codeletion and mutation. Immune infiltration analysis further showed that the high risk score was negatively correlated with activated NK cell and monocyte counts, but positively correlated with macrophage and activated dendritic cell counts and higher mRNA expression. Here, a novel gene signature based on DEGs between GBM and healthy SVZ was developed for determining GBM patient prognosis. Targeting these genes may be a therapeutic strategy for GBM.
研究表明，胶质母细胞瘤（GBM）细胞起源于脑室下区（SVZ），且GBM与SVZ的接触与较差的预后和较高的复发率相关。然而，关于GBM与SVZ之间差异表达基因（DEG）的研究尚缺乏。我们对7例累及SVZ的GBM以及配对的无肿瘤SVZ组织进行了深度RNA测序。评估了DEG和富集情况。我们从中国胶质瘤基因组图谱（CGGA）和癌症基因组图谱（TCGA）数据库中获取了GBM患者的表达谱和临床数据。利用最小绝对收缩和选择算子Cox回归模型在CGGA队列中构建多基因特征。来自TCGA队列的GBM患者数据用于验证。我们在GBM与健康SVZ样本之间鉴定出137个DEG（97个上调和40个下调）。富集分析显示，DEG主要富集于免疫相关术语，包括体液免疫反应调节、T细胞分化以及对肿瘤坏死因子的反应，以及MAPK、cAMP、PPAR、PI3K-Akt和NF-κb信号通路。构建了一个八基因（，，，，，，，和）特征。GBM患者被分为两个风险组。高风险患者与低风险患者相比，总生存期显著缩短。单因素和多因素回归分析表明，风险评分水平是一个独立的预后因素。GBM患者的高风险评分与1p19q共缺失和突变呈负相关。免疫浸润分析进一步表明，高风险评分与活化NK细胞和单核细胞计数呈负相关，但与巨噬细胞和活化树突状细胞计数以及较高的mRNA表达呈正相关。在此，基于GBM与健康SVZ之间的DEG开发了一种新的基因特征，用于确定GBM患者的预后。靶向这些基因可能是GBM的一种治疗策略。
