Institute of Automation Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100190, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa365.
Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.
胶质母细胞瘤（GBM）是一种常见的恶性脑肿瘤，常与中枢神经系统（CNS）疾病合并存在。CNS 疾病和 GBM 细胞都会释放谷氨酸，表现出异常，但细胞行为不同。因此，它们的病因尚不清楚，也不清楚 CNS 疾病如何影响 GBM 的行为或生长。这促使我们采用定量分析框架，利用从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据集获得的数据集，来揭示可能将 CNS 疾病和 GBM 联系起来的共同差异表达基因（DEG）和细胞信号通路。在确定 DEG 后，我们鉴定了疾病-基因关联网络和信号通路，并进行了基因本体（GO）分析以及枢纽蛋白鉴定，以预测这些 DEG 的作用。我们通过利用 Cox 比例风险模型和 Kaplan-Meier 估计器挖掘临床和遗传因素，将研究扩展到确定可能在 GBM 进展和 GBM 患者生存中发挥作用的重要基因。在这项研究中，确定了 177 个 DEG，其中 129 个上调基因和 48 个下调基因。我们的研究结果表明了 CNS 疾病可能影响 GBM 进展、生长或建立的新方式，也可能作为 GBM 预后的生物标志物和治疗的潜在靶点。我们与黄金标准数据库的比较也提供了进一步的证据支持我们在 GBM 进展的病理基础中确定的生物标志物的关联性。
