Zhang Moxuan, Zhang Quan, Bai Jilin, Zhao Zhiming, Zhang Jian
Department of Neurosurgery, Linyi People's Hospital, Linyi 276000, China.
Weifang Medical University, Weifang 261053, China.
Math Biosci Eng. 2021 Mar 2;18(3):2077-2096. doi: 10.3934/mbe.2021107.
Gliomas are common malignant tumors of the central nervous system. Despite the surgical resection and postoperative radiotherapy and chemotherapy, the prognosis of glioma remains poor. Therefore, it is important to reveal the molecular mechanisms that promotes glioma progression. Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify 428 differentially expressed genes (DEGs) and a core module from three microarray datasets. Heat maps were drawn based on DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database. The core module was significantly involved in several KEGG pathways, such as "cell cycle", "viral carcinogenesis", "progesterone-mediated oocyte maturation", "p53 signaling pathway". The protein-protein interaction (PPI) networks and modules were built using the STRING database and the MCODE plugin, respectively, which were visualized using Cytoscape software. Identification of hub genes in the core module using the CytoHubba plugin. The top modular genes AURKA, CDC20, CDK1, CENPF, and TOP2A were associated with glioma development and prognosis. In the Human Protein Atlas (HPA) database, CDC20, CENPF and TOP2A have significant protein expression. Univariate and multivariate cox regression analysis showed that only CENPF had independent influencing factors in the CGGA database. GSEA analysis found that CENPF was significantly enriched in the cell cycle, P53 signaling pathway, MAPK signaling pathway, DNA replication, spliceosome, ubiquitin-mediated proteolysis, focal adhesion, pathway in cancer, glioma, which was highly consistent with previous studies. Our study revealed a core module that was highly correlated with glioma development. The key gene CENPF and signaling pathways were identified through a series of bioinformatics analysis. CENPF was identified as a candidate biomarker molecule.
胶质瘤是中枢神经系统常见的恶性肿瘤。尽管进行了手术切除以及术后放疗和化疗，但胶质瘤的预后仍然很差。因此，揭示促进胶质瘤进展的分子机制很重要。从基因表达综合数据库（GEO）获取微阵列数据集。使用GEO2R工具从三个微阵列数据集中鉴定出428个差异表达基因（DEG）和一个核心模块。基于DEG绘制热图。使用DAVID数据库进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。该核心模块显著参与了多个KEGG通路，如“细胞周期”、“病毒致癌作用”、“孕酮介导的卵母细胞成熟”、“p53信号通路”。分别使用STRING数据库和MCODE插件构建蛋白质-蛋白质相互作用（PPI）网络和模块，并使用Cytoscape软件进行可视化。使用CytoHubba插件在核心模块中鉴定枢纽基因。顶级模块基因AURKA、CDC20、CDK1、CENPF和TOP2A与胶质瘤的发生发展和预后相关。在人类蛋白质图谱（HPA）数据库中，CDC20、CENPF和TOP2A有显著的蛋白质表达。单因素和多因素cox回归分析表明，在CGGA数据库中只有CENPF具有独立影响因素。基因集富集分析（GSEA）发现CENPF在细胞周期、P53信号通路、丝裂原活化蛋白激酶（MAPK）信号通路、DNA复制、剪接体、泛素介导的蛋白水解、粘着斑、癌症通路、胶质瘤中显著富集，这与先前的研究高度一致。我们的研究揭示了一个与胶质瘤发生发展高度相关的核心模块。通过一系列生物信息学分析鉴定出关键基因CENPF和信号通路。CENPF被鉴定为候选生物标志物分子。
