Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
BMC Cancer. 2023 Aug 14;23(1):749. doi: 10.1186/s12885-023-11131-7.
Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth.
The growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in the Cancer Genome Atlas cohort (TCGA) was used to construct growth-related genes subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately.
Our research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides we showed that Foretinib may be a potential therapeutic agent for GGS1, the worst prognostic subtype, through data screening and in vitro experiments. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression.
These results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.
胶质母细胞瘤（GBM）是一种高度恶性的脑肿瘤，其特点是存在显著的肿瘤内异质性，即在肿瘤组织内存在高度的可变性。尽管近年来已经确定了 GBM 的几种亚型，但仍需要探索一种基于与增殖和生长相关的基因的分类方法。
通过 CRISPR-Cas9 和单变量 Cox 回归分析确定 GBM 的生长相关基因。使用癌症基因组图谱队列（TCGA）中的这些基因的表达来通过共识聚类构建生长相关基因亚型（GGS）。使用两个独立的基因表达综合组学（GEO）队列和 ZZ 队列中的最近模板预测（NTP）算法对这种亚分型进行验证。此外，还分别分析了每个不同亚型的拷贝数变异、生物学功能和潜在药物。
我们的研究建立了多中心验证的 GGS。GGS1 表现出最差的预后，其 chr7 增益和 chr10 缺失的频率最高，chr19 和 chr20 共同增益的频率最低。此外，GGS1 表现出最高的 EGFR 表达。此外，它在代谢、干细胞、增殖和信号通路中显著富集。除此之外，我们通过数据筛选和体外实验表明，Foretinib 可能是预后最差的 GGS1 亚型的潜在治疗药物。GGS2 的预后中等，chr7 增益和 chr10 缺失的比例略高，chr19 和 chr20 共同增益的比例最高。GGS3 的预后最好，chr7 增益和 10 缺失及 EGFR 表达最少。
这些结果增强了我们对 GBM 异质性的理解，为 GBM 患者的分层管理和精准治疗提供了思路。
