Selected by gene co-expression network and molecular docking analyses, ENMD-2076 is highly effective in glioblastoma-bearing rats
Selected by gene co-expression network and molecular docking analyses, ENMD-2076 is highly effective in glioblastoma-bearing rats
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通过基因共表达网络和分子对接分析选择，ENMD-2076 对胶质母细胞瘤大鼠非常有效
DOI：
10.18632/aging.102422
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发表时间：
2019-11-15
期刊：
影响因子：
5.2
通讯作者：
Zhao, Gang
中科院分区：
文献类型：
--
作者：
Zhong, Sheng;Bai, Yang;Zhao, Gang
背景：胶质母细胞瘤是最常见的恶性脑肿瘤类型。生物信息学技术和结构生物学被有效地和系统地用于识别恶性肿瘤的特定靶点和筛选潜在的药物。结果：GBM患者AURKA和KDR基因的表达水平明显高于正常对照组。然后，我们确定了一个小分子化合物，ENMD-2076，可以有效地抑制极光激酶A和VEGFR-2(由KDR编码)的活性。ENMD-2076预测没有毒性特性，也具有吸收和令人满意的脑/血屏障穿透能力。进一步的结果表明，ENMD-2076能显著抑制GBM细胞的增殖和活力，并能抑制GBM细胞的迁移和侵袭。ENMD-2076通过抑制PI3K/AKT/mTOR信号通路，诱导胶质母细胞瘤细胞周期停滞于G2-M期，诱导细胞凋亡。此外，ENMD-2076延长了荷瘤大鼠的中位生存期，并抑制了体内肿瘤体积的生长速度。结论：ENMD-2076是治疗胶质母细胞瘤的一种有前景的药物，具有广阔的应用前景。方法：利用生物信息学技术，包括WGCNA分析、PPI网络分析、GO分析、KEGG分析和GSEA分析，证明AURKA和KDR基因是胶质母细胞瘤的中枢驱动基因。在通过虚拟筛选分析确定化合物后，进一步进行实验以检测该化合物在体内和体外的抗胶质母细胞瘤活性。
Background: Glioblastoma is the most common type of malignant brain tumor. Bioinformatics technology and structure biology were effectively and systematically used to identify specific targets in malignant tumors and screen potential drugs. Results: GBM patients have higher AURKA and KDR mRNA expression compared with normal samples. Then, we identified a small molecular compound, ENMD-2076, could effectively inhibit Aurora kinase A and VEGFR-2 (encoded by KDR) activities. ENMD-2076 is predicted without toxic properties and also has absorption and gratifying brain/blood barrier penetration ability. Further results demonstrated that ENMD-2076 could significantly inhibit GBM cell lines proliferation and vitality, it also suppressed GBM cells migration and invasion. ENMD-2076 induced glioblastoma cell cycle arrest in G2-M phase and apoptosis by inhibiting PI3K/AKT/mTOR signaling pathways. Additionally, ENMD-2076 prolonged the median survival time of tumor-bearing rats and restrained growth rate of tumor volume in vivo. Conclusions: Our findings reveal that ENMD-2076 is a promising drug in dealing with glioblastoma and have a perspective application. Methods: We show that AURKA and KDR genes are hub driver genes in glioblastoma with bioinformatics technology including WGCNA analysis, PPI network, GO, KEGG analysis and GSEA analysis. After identifying a compound via virtual screening analysis, further experiments were carried out to examine the anti-glioblastoma activities of the compound in vivo and in vitro.