Xia Zhiyu, Gao Peng, Chen Yu, Shu Lei, Ye Lei, Cheng Hongwei, Dai Xingliang, Hu Yangchun, Wang Zhongyong
Department of Clinical Medicine, the First Clinical College of Anhui Medical University, Hefei, China.
Department of Neurosurgery, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
Transl Cancer Res. 2022 May;11(5):1386-1405. doi: 10.21037/tcr-22-1122.
To analyze the key prognostic genes and potential traditional Chinese medicine targets in glioblastoma (GBM) by bioinformatics and network pharmacology.
GBM datasets were obtained from the Gene Expression Omnibus (GEO) database to clarify the differentially-expressed genes (DEGs) in the carcinoma and paracancerous tissues. The molecular functions (MF) and signaling pathways of enriched DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The STRING database and Cytoscape software were used to construct the protein-protein interaction (PPI) network and screen hub genes to focus on genes with greater clinical significance. The transcription expression and prognosis of hub genes were analyzed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. The important compounds and target molecules were obtained via the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) database. We identified the active ingredients by setting the property values of pharmacokinetic attribute values. We constructed the network of "Chinese medicine ingredients-DEGs target" and screened out the target genes and active ingredients with high correlation scores. Finally, molecular docking verification was carried out using AutoDock Tools and PyMOL.
We obtained 271 DEGs, including 212 up-regulated genes and 59 down-regulated genes and screened ten hub genes. GO and KEGG analyses suggested that the hub genes were mainly involved in the following biological processes: the cell cycle, cell division, and cell adhesion, as well as extracellular matrix adhesion-related pathways, the p53 signaling pathways, and cadherin binding involved in cell-cell adhesion. We established the interaction network between the components and DEGs to screen out the traditional Chinese medicine active component (luteolin) and target genes (BIRC5 and CCNB1) for the treatment of GBM. The molecular docking results showed that the bindings of protein receptors, BIRC5 and CCNB1, with the compound ligand, luteolin, were stable and formed by hydrogen bonding interaction.
In this study, we determined that luteolin potentially inhibits glioblastoma proliferation and migration through key target genes, BIRC5 and CCNB1, via bioinformatics and network pharmacology analysis, and affects the prognosis of GBM patients, providing new ideas for clinical targeted therapy and new drug development.
通过生物信息学和网络药理学分析胶质母细胞瘤（GBM）中的关键预后基因和潜在的中药靶点。
从基因表达综合数据库（GEO）获取GBM数据集，以阐明癌组织和癌旁组织中的差异表达基因（DEGs）。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析来分析富集的DEGs的分子功能（MF）和信号通路。利用STRING数据库和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络并筛选枢纽基因，以关注具有更大临床意义的基因。使用基因表达谱交互式分析2（GEPIA 2）数据库分析枢纽基因的转录表达和预后。通过中药系统药理学数据库（TCMSP）数据库获得重要化合物和靶分子。我们通过设置药代动力学属性值的属性值来鉴定活性成分。构建“中药成分-DEGs靶点”网络，筛选出相关性得分高的靶基因和活性成分。最后，使用AutoDock Tools和PyMOL进行分子对接验证。
我们获得了271个DEGs，包括212个上调基因和59个下调基因，并筛选出10个枢纽基因。GO和KEGG分析表明，枢纽基因主要参与以下生物学过程：细胞周期、细胞分裂和细胞黏附，以及细胞外基质黏附相关途径、p53信号通路和参与细胞间黏附的钙黏蛋白结合。我们建立了成分与DEGs之间的相互作用网络，筛选出用于治疗GBM的中药活性成分（木犀草素）和靶基因（BIRC5和CCNB1）。分子对接结果表明，蛋白质受体BIRC5和CCNB1与化合物配体木犀草素的结合稳定，且通过氢键相互作用形成。
在本研究中，我们通过生物信息学和网络药理学分析确定，木犀草素可能通过关键靶基因BIRC5和CCNB1抑制胶质母细胞瘤的增殖和迁移，并影响GBM患者的预后，为临床靶向治疗和新药开发提供新思路。
