Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London SE1 9RT, UK.
Science for Life Laboratory, Royal Institute of Technology (KTH), SE-10691 Stockholm, Sweden.
Int J Mol Sci. 2021 Dec 8;22(24):13213. doi: 10.3390/ijms222413213.
Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.
胶质母细胞瘤(GBM)是最恶性的中枢神经系统肿瘤之一,预后不良,生存率低。因此,解析 GBM 进展中涉及的潜在分子机制,并鉴定负责疾病进展的关键驱动基因,对于发现潜在的诊断标志物和治疗靶点至关重要。在这种情况下,需要获得各种生物数据、开发新的方法学,并为多组学数据的整合生成生物网络,以便深入了解 GBM 的发生和进展。系统生物学方法在分析异质高通量组学数据、提取生物医学数据中的重要信息和生成新假设方面变得不可或缺。本综述提供了关于 GBM 的最新知识,并讨论了 GBM 中的多组学数据和最近的系统分析,以确定关键的生物学功能和基因。这些知识可用于开发有效的诊断和治疗策略,也可用于实现 GBM 的个性化医疗。