Abstract：
The objective of this study was to analyze potential targets of metformin against ovarian cancer (OC) through network pharmacology. Pharmacodynamic targets of metformin were predicted using the Bioinformatics Analysis Tool for the molecular mechanism of traditional Chinese medicine (BATMAN), Drugbank, PharmMapper, SwissTargetPrediction, and TargetNet databases. R was utilized to analyze the gene expression of OC tissues, normal/adjacent noncancerous tissues, and screen differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) + Genotype-Tissue Expression (GTEx) datasets. STRING 11.0 was utilized to explore the protein-protein interaction (PPI) of metformin target genes differentially expressed in OC. Cytoscape 3.8.0 was used to construct the network and screen the core targets. Additionally, gene ontology (GO) annotation and enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the common targets of metformin and OC through the DAVID 6.8 database. A total of 95 potential common targets of metformin and OC were identified from the intersection of 255 potential pharmacodynamic targets of metformin and 10,463 genes associated with OC. Furthermore, 10 core targets were screened from the PPI network [e.g., interleukin (IL) 1B, KCNC1, ESR1, HTR2C, MAOB, GRIN2A, F2, GRIA2, APOE, PTPRC]. In addition, it was shown in GO enrichment analysis that the common targets were mainly associated with biological processes (i.e., response to stimuli or chemical, cellular processes, and transmembrane transport), cellular components (i.e., plasma membrane, cell junction, and cell projection), and molecular functions (i.e., binding, channel activities, transmembrane transporter activity, and signaling receptor activities). Furthermore, it was indicated by KEGG pathway analysis that the common targets were enriched in metabolic pathways. The critical molecular targets and molecular pathways of metformin against OC were preliminarily determined by bioinformatics-based network pharmacology analysis, providing a basis, and reference for further experimental studies.
摘要：
本研究的目的是通过网络药理学分析二甲双胍抗卵巢癌（OC）的潜在靶点。使用中药分子机制的生物信息学分析工具（BATMAN）预测二甲双胍的药效学目标，药店,PharmMapper,SwissTargetPrediction,和TargetNet数据库。R用于分析OC组织的基因表达，正常/邻近非癌组织，并在基因表达综合(GEO)和癌症基因组图谱(TCGA)+基因型-组织表达(GTEx)数据集中筛选差异表达基因(DEGs)。STRING11.0用于探索在OC中差异表达的二甲双胍靶基因的蛋白质-蛋白质相互作用（PPI）。使用Cytoscape3.8.0构建网络并筛选核心目标。此外,通过DAVID6.8数据库对二甲双胍和OC的常见靶标进行基因本体论（GO）注释和富集以及京都基因和基因组百科全书（KEGG）途径富集分析。从255个潜在的二甲双胍药效学靶标和10,463个与OC相关的基因的交集中，共鉴定出95个潜在的二甲双胍和OC的共同靶标。此外，从PPI网络中筛选出10个核心目标[例如，白细胞介素（IL）1B，KCNC1,ESR1,HTR2C,MAOB,GRIN2A,F2,GRIA2,APOE,PTPRC]。此外,GO富集分析显示，常见靶标主要与生物过程相关(即，对刺激或化学物质的反应，细胞过程，和跨膜运输)，细胞成分(即，质膜，细胞连接，和细胞投影)，和分子功能(即，绑定,渠道活动,跨膜转运蛋白活性，和信号受体活性)。此外，KEGG途径分析表明,常见靶标富集在代谢途径中.通过基于生物信息学的网络药理学分析,初步确定了二甲双胍抗OC的关键分子靶点和分子通路,提供基础,并为进一步的实验研究提供参考。
