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一种与MCL-1相关的胶质母细胞瘤预后特征的开发及抑制剂筛选

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma.

作者信息

Zhang Ao, Guo Zhen, Ren Jia-Xin, Chen Hongyu, Yang Wenzhuo, Zhou Yang, Pan Lin, Chen Zhuopeng, Ren Fei, Chen Youqi, Zhang Menghan, Peng Fei, Chen Wanting, Wang Xinhui, Zhang Zhiyun, Wu Hui

机构信息

Department of Neurology, The First Hospital of Jilin University, Changchun, China.

Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

出版信息

Front Pharmacol. 2023 Jul 19;14:1162540. doi: 10.3389/fphar.2023.1162540. eCollection 2023.

Abstract

The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors. In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected. Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors. This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

摘要

胶质母细胞瘤(GBM)的传统治疗方法效果不佳,患者预后较差。MCL-1在GBM中的表达显著增加,在靶向治疗中具有较高的应用价值。在本研究中,我们对胶质母细胞瘤患者的预后进行了预测,构建了MCL-1相关预后特征(MPS)并研发了MCL-1小分子抑制剂。本研究从TCGA网站获取了168例GBM样本的RNA测序和临床数据,并进行了免疫分析、差异基因表达分析和功能富集分析。随后,通过LASSO Cox分析构建并验证了MCL-1相关预后特征(MPS),并构建了列线图以预测患者的预后。最后,利用LibDock、ADME、TOPKAT和CDOCKER模块以及Discovery Studio2019软件中的分子动力学模拟,对从ZINC15数据库下载的17931个小分子进行筛选,最终筛选出两种更安全、更有效的小分子抑制剂。免疫分析显示MCL1_H组存在免疫抑制,免疫检查点抑制剂治疗具有积极作用。差异表达基因分析鉴定出449个差异表达基因。使用LASSO Cox分析构建并验证MPS。使用TSHR HIST3H2A、ARGE OSMR、ARHGEF25构建风险评分,证明低风险组患者预后较好。单因素和多因素分析证明风险可作为患者预后的独立预测因子。构建列线图以预测患者1、2、3年的生存概率。利用一系列计算机辅助技术,虚拟筛选出另外两种更合理的先导化合物ZINC000013374322和ZINC000001090002。这些化合物对MCL-1具有潜在抑制作用,为MCL-1特异性小分子抑制剂的设计和进一步研发提供了依据。本研究从免疫学角度分析了MCL-1对胶质母细胞瘤患者预后的影响,构建了新的预后模型以评估患者的生存率,并进一步筛选出2种MCL-1小分子抑制剂,为胶质母细胞瘤的治疗和预后提供了新思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/855c/10394558/96167ed51046/fphar-14-1162540-g001.jpg

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