基于网络药理学、分子对接及实验验证探讨黄柏治疗痛风的作用机制CSCD

采用网络药理学、分子对接和体外细胞实验探讨黄柏抗痛风(gout)的物质基础与潜在作用机制。首先通过TCMSP数据库获得黄柏主要活性成分及其对应作用靶点信息;通过GeneCards、OMIM、TTD数据库获得痛风相关疾病靶点;将黄柏有效成分对应靶点与痛风靶点取交集,借助STRING平台及Cytoscape3.9.0软件,绘制交集基因蛋白互作(PPI)网络图;利用基因注释与分析平台(Metascape)数据库对核心靶点进行基因本体(GO)功能及京都基因与基因组百科全书(KEGG)通路富集分析,通过微生信云平台对富集结果可视化;借助AutoDock Tools软件对核心成分及关键靶点基因进行分子对接,并对核心化学成分抗痛风炎症作用进行实验验证。共筛选出25个黄柏抗痛风活性成分和70个关键交集靶点,PPI网络分析获得5个关键靶点包括蛋白激酶B1(AKT1)、肿瘤坏死因子(TNF)、过氧化物酶体增生激活受体γ(PPARγ)、白介素6(IL-6)、前列腺素内过氧化物合酶(PTGS 2);GO功能和KEGG通路富集显示,黄柏作用于细胞迁移的正向调控、细胞分化的负调控、炎症反应等生物学过程,调控PI3K-Akt、MAPK等信号通路,进而发挥抗痛风作用。分子对接结果显示,黄柏的5个主要活性成分与关键靶点间存在分子结合位点且结合能较强,均小于-5 kcal/mol;体外实验显示核心化学成分对尿酸钠诱导的炎症反应有较好的抑制作用,本研究初步揭示了黄柏具有多种潜在的抗痛风活性成分,其作用机理可能是通过作用于多靶点和多通路来实现的。     

基于网络药理学和分子对接探索桑白皮治疗糖尿病周围神经病变的潜在机制

本研究运用网络药理学和分子对接方法对中药桑白皮治疗糖尿病周围神经病变(DPN)的活性成分、潜在作用靶点和信号通路进行研究,探索桑白皮治疗DPN的可能作用机制。首先从中药系统药理学数据库(TCMSP)筛选出桑白皮的活性成分及靶点基因。通过GeneCards数据库及OMIM数据库筛选出DPN的疾病靶点基因,并用Cytoscape软件构建“药物-有效成分-靶基因-疾病”中药调控网络图。将有效成分靶标与疾病靶标上传到STRING数据库,构建蛋白互作网络图(PPI),并使用R语言对得到的PPI进行核心基因的筛选。运用R语言对关键靶点进行GO富集分析和KEGG通路富集分析。其次从活性成分及靶点基因中根据degree值筛选出前3个关键成分,并将该网络中的基因靶点以degree值高低进行排序,选择前3个核心靶点,然后从RCSB数据库下载相关蛋白的结构,使用Pymol软件去除溶剂分子与配体,使用AutoDock软件进行分子对接。最后通过酶联免疫吸附实验和荧光光谱实验验证网络药理学富集分析的结果。最终预测到31个桑白皮活性成分,312个活性成分相关靶点,120个桑白皮-糖尿病周围神经病变共同有效靶点。活性成分中度值最高的为槲皮素,其次为山柰酚。PPI网络核心基因为转录因子AP-1(JUN)、丝裂原活化蛋白激酶1(MAPK1)、转录因子p65(RELA)、丝氨酸-苏氨酸蛋白激酶1(AKT1)、白介素6(IL-6)等;GO富集分析显示会影响基因的转录、细胞因子表达和蛋白激酶活性等;KEGG通路富集分析显示AGE-RAGE信号通路、流体剪切力和动脉粥样硬化为显著性最高的通路,其次为卡波西肉瘤相关疱疹病毒感染、MAPK信号通路、人巨细胞病毒感染、TNF信号通路。分子对接结果显示关键成分中槲皮素与对应靶点具有较好的结合活性。酶联免疫吸附实验提示桑白皮能够降低IL-6和TNF-α的表达,荧光光谱实验证实桑白皮能够减少AGEs。可见中药桑白皮治疗糖尿病周围神经病变具有多成分、多靶点、多功能、多通路的作用特点,其潜在的作用机制可能与AGE-RAGE信号通路、肿瘤坏死因子信号通路等有关。     

基于网络药理学和分子对接探讨银杏叶治疗高血压病潜在作用机制

通过网络药理学和分子对接技术探讨银杏叶治疗高血压的潜在作用机制。首先,通过TCMSP、Swiss Target Prediction、Uniprot等数据库获取银杏叶的化学成分与对应靶点;运用OMIM、DrugBank及Gencards疾病数据库搜索高血压相关靶点。然后,取银杏叶对应靶点与高血压相关靶点的交集即可得到银杏叶治疗高血压病的有效靶点,使用STRING数据库对交集靶点进行蛋白相互作用(PPI)网络预测,筛选发挥治疗作用的关键成分与关键靶点。最后利用DAVID数据库对关键靶点进行GO和KEGG富集分析,揭示银杏叶治疗高血压病的作用机制。对筛选出的关键成分与治疗高血压关键靶点进行分子对接验证。共筛选出银杏叶治疗高血压病的活性成分21个,靶点190个。PPI网络分析结果显示,银杏叶治疗高血压关键成分有槲皮素、山柰酚、木犀草素、异鼠李素、金圣草黄素5个,关键靶点有PTGS2、AKTI、EGFR、TNF等20个。GO和KEGG分析结果显示,银杏叶治疗高血压病的靶点显著富集于乙型肝炎、TNF信号通路、Toll样受体信号通路、HIF-1通路、MAPK信号通路等通路。分子作用结果显示,木犀草素、金圣草黄素、异鼠李素等与PTGS2、AKTI、EFGR具有较强的亲和力。该研究初步揭示了银杏叶具有多成分、多靶点、多通路治疗高血压的潜在作用机制,为银杏叶治疗高血压物质基础及作用机制的进一步研究奠定基础。     

当归芍药散对新型冠状病毒肺炎的临床疗效及其作用机制的网络药理学研究

为探讨当归芍药散对新型冠状病毒肺炎(COVID-19)的临床疗效及其作用机制,随机选取2020年1月20日~3月20日武汉协和医院收治的100例COVID-19患者作为研究对象,观察当归芍药散联合常规治疗前后患者的基本体征、主要症状、实验室指标、核酸转阴、CT影像学的变化情况,发现以上症状及指标均较前明显好转。同时运用网络药理学分析当归芍药散治疗COVID-19的潜在作用靶点,得到作用靶点18个,GO功能富集180条目,KEGG通路富集109条。结果提示当归芍药散可能通过多靶点、多通路发挥抗病毒、抗炎、抗氧化等作用,进而达到治疗COVID-19的效果。     

化湿败毒方治疗新型冠状病毒肺炎的药理学机制探讨和网络药理学研究

本文旨在探讨化湿败毒方治疗COVID-19的现代药理学研究并运用网络药理学方法预测化湿败毒方治疗COVID-19的作用机制。通过查阅文献及临床报道,总结化湿败毒方治疗COVID-19现代药理学研究。通过TCMSP数据库获取化湿败毒方的主要活性化合物及对应靶点并利用GeneCards数据库获取COVID-19的疾病靶点。使用STRING数据库构建蛋白互作网络并对核心靶点进行GO富集和KEGG通路分析,利用Cytoscape3.7.0软件构建化合物-靶标网络。最后把主要核心化合物与SARS-CoV-2 3CL水解酶及ACE2受体进行分子对接。现代药理学研究表明,化湿败毒方有抗炎、抗病毒和调节免疫作用。结果筛选到261个中药靶点,251个疾病相关靶点,药物和疾病靶点取交集得到关键靶点49个。GO富集包括1 547条生物过程、29条细胞组分以及86项分子功能,KEGG通路富集得156条通路与COVID-19相关(P0.05),涉及卡波西氏肉瘤相关疱疹病毒感染、人巨细胞病毒感染、甲型流感、IL-17通路、TNF通路、AGE-RAGE通路等相关通路。预测出主要的核心化合物有槲皮素、木犀草素、山奈酚、汉黄芩素、柚皮素、β-谷甾醇、黄芩素等,在整个网络中发挥着关键作用。分子对接结果显示槲皮素、木犀草素、山奈酚与3CL水解酶和ACE2均有较好的结合。本研究较为全面揭示了化湿败毒方治疗COVID-19"多成分、多靶点、多通路"的特点,为深入探讨化湿败毒方治疗COVID-19的作用机制提供参考依据。     

基于网络药理学和分子对接探索荆芥-防风药对治疗冠状病毒肺炎的潜在机制

本文旨在通过网络药理学和分子对接探索荆芥-防风药对治疗冠状病毒肺炎的潜在药效物质和作用靶点。首先,通过检索TCMSP、ETCM、BATMAN-TCM数据库收集荆芥-防风中活性成分及其作用靶点,并在GeneCards、OMIM、NCBI Gene数据库收集冠状病毒肺炎相关靶点。然后,两者取交集运用STRING数据库分析关键靶点间蛋白相互作用,并利用DAVID数据库进行生物功能和通路分析。最后,利用Autodock软件对潜在药效物质和关键靶点进行分子对接。本研究共收集到28个活性成分、56个关键靶点。GO功能富集收集到176个生物过程(biological process)、47个分子功能(cell compound)、36个细胞组分(molecular function)(P0.05)。KEGG通路富集共收集到99条通路(P0.05)。分子对接结果显示,潜在药效物质与关键靶点及血管紧张素转化酶II、COVID-19 main protease对接结果能量低于-5 kcal/mol。本文揭示了荆芥-防风药对治疗冠状病毒肺炎可能的潜在药效物质和作用靶点,为荆芥-防风的开发和后续研究打下了基础。     

基于网络药理学与分子对接技术的清瘟护肺颗粒防治新型冠状病毒肺炎(COVID-19)的潜在药效物质研究

本文通过网络药理学和分子对接技术探讨清瘟护肺颗粒防治新型冠状病毒肺炎(COVID-19)的潜在药效物质。首先,通过TCMSP数据库,BATMAN-TCM数据库及TCMIP数据库检索清瘟护肺颗粒中十六味药的化学成分及作用靶点,利用GeneCards和OMIM数据库检索COVID-19的相关疾病靶点。然后,通过venny2.1.0获取清瘟护肺颗粒防治COVID-19的潜在靶点,利用R语言对潜在靶点进行GO功能和KEGG通路富集分析,并结合文献对富集所得通路进行分析。最后,利用Cytoscape3.7.1软件构建网络图,采用AutoDock4.2.1软件评价清瘟护肺颗粒中潜在药效成分和新型冠状病毒SARS-CoV-23CL水解酶、血管紧张素转化酶II(ACE2)和RNA依赖的RNA聚合酶(RdRp)的结合作用。网络药理学得到清瘟护肺颗粒防治COVID-19的473个活性成分和123个靶点,KEGG结果及文献分析预测到清瘟护肺颗粒可通过调控MAPK、小细胞肺癌、肺结核、PI3K-AKT等多条信号通路而发挥作用,分子对接结果显示清瘟护肺颗粒中潜在药效成分和SARS-CoV-23CL水解酶、ACE2及RdRp具有良好的亲和性。本研究较为全面揭示了清瘟护肺颗粒治疗COVID-19“多成分、多靶点、多通路”的特点,为深入探讨清瘟护肺颗粒治疗COVID-19的作用机制提供参考依据。     

基于网络药理学清肺排毒汤治疗新冠肺炎的物质基础及作用机制研究

本研究运用网络药理学方法研究清肺排毒汤治疗新冠肺炎(COVID-19)的多成分、多靶点、多通路的作用机制,旨在为相关基础研究及临床应用提供依据。首先利用TCMSP数据库根据OB≥30%及DL≥0.18为筛选条件,筛选清肺排毒汤中活性成分302个及相关作用靶点148个,通过GeneCards数据库筛选COVID-19相关作用靶点362个,经Venn分析得到交集靶点23个。接着利用Cytoscape软件的CentiScaPe插件分析得到显著有效化合物10个和关键靶点5个,进一步构建网络拓扑图。通过对显著有效化合物和关键靶点的分子对接,结果显示,分子间的结合能力和相互作用能力较强。最后通过R软件的ClusterProfiler包对关键靶点做GO富集和KEGG通路富集分析,显著有效化合物中槲皮素、木犀草素、柚皮素、山奈酚、黄芩素等具有不同程度的止咳、祛痰、消炎、抗病毒等功效,关键靶点主要被富集在IL-17、Tuberculosis、TNF、MAPK、Th17、Pertussis等144个相关信号通路上,涉及磷酸酶结合、MAP激酶活性、细胞因子受体结合等28个生物学功能调控机体代谢、免疫调节、炎症反应等生理过程,根据以上结果,认为清肺排毒汤的活性成分对COVID-19的治疗具有多靶点、多途径的调节作用。     

参苓白术散治疗结直肠癌的网络药理学机制及分子靶点探讨

为探究参苓白术散(Shenling Baizhu powder, SLBZP)治疗结直肠癌的作用机制。运用网络药理学的方法,通过TCMSP数据库收集、筛选SLBZP的活性成分并获得作用靶点,GEO数据库筛选获得CRC患者与健康个体之间的差异表达基因作为CRC的相关靶点。借助Cytoscape软件构建药物成分、疾病靶点网络,Bisogenet构建蛋白质相互作用(PPI)网络,以识别SLBZP作用于CRC的候选靶点。通过基因本体(GO)功能富集京都基因与基因组百科全书(KEGG)通路富集分析核心基因的生物学功能及通路富集情况。Cytoscape软件构建了靶点-途径网络,根据Degree筛选关键靶基因。通过分析得到SLBZP治疗CRC的核心靶点165个,发现核心靶点功能注释与转录因子的活性、蛋白质稳定性调节、泛素蛋白连接酶结合等有关。PI3K-Akt信号通路,PD-L1/PD-1途径,病毒致癌等二十个途径均得到显著的富集。AKT1、TP53、PIK3R1为核心基因,MAPK3、NFKB1、CCND1、MAPK1、RELA、CDKN1A、MYC、STAT3、MDM2、JUN、RB1等是SLBZP网络途径中治疗CRC的关键基因。综上SLBZP对CRC的治疗作用可能与特定的生物学过程及相关途径调节炎性反应、优化肠道菌群结构发挥治疗作用。通过网络药理学分析评估,SLBZP复杂的作用机理及作用靶标得到了进一步的揭示,对CRC治疗意义重大。     

基于网络药理学联合分子对接对逍遥散干预原发性肝癌的作用机制研究

为探索逍遥散干预原发性肝癌(HCC)的作用机制。运用网络药理学的方法,通过中药系统药理学技术平台(TCMSP)网站检索逍遥散的主要活性成分及对应的作用靶点,再利用GEO数据库筛选并分析原发性肝癌的差异基因,采用Cytoscape3.7.2软件构建活性成分—靶点网络图,利用Biso Genet插件分析筛选得出PPI蛋白互作网络和PPI网络的关键节点,然后运用生物信息学的方法对关键靶点进行基因功能分析和通路富集分析。最后使用分子对接技术对化合物核心成分与关键靶点进行对接验证。通过分析得到逍遥散作用于HCC的交集基因27个,GO富集显示逍遥散的生物功能主要设涉及无机物质细胞反应、趋化因子活动、细胞周期性蛋白依赖复合物等方面;KEGG通路富集显示逍遥散影响的通路主要有细胞衰老信号通路、甾类激素生物合成信号通路、p53信号通路等。分子对接结果显示逍遥散核心成分与HCC关键靶点亲和力良好。综上逍遥散改善HCC存在多成分、多靶点和多重药理作用,为进一步研究治疗HCC提供了线索。     

IgA 肾病患者与膜性肾病患者外周血单核细胞mRNA分析

为了寻找诊断、鉴别IgA肾病(IgAN)和膜性肾病(MN)的血液特异性标记物,利用公共数据库中的IgAN和MN患者的外周血单核细胞(PBMCs)的转录组表达谱数据集识别特异性生物标记物,为诊断和鉴别提供简便、可靠的依据补充。从公共基因表达数据库(GEO)下载IgAN患者组(n=15)和MN患者组(n=8)芯片数据集,筛选前250个差异表达基因(DEGs)。通过分析筛选关键基因和途径,进行基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)通路分析和蛋白质与蛋白质相互作用关系(PPI)分析等进一步了解DEGs。通过分析共发现75个显著DEGs,其中73个上调基因,2个下调基因。GO富集分析的生物学过程(BP)主要包括蛋白质转运、内溶酶体到溶酶体转运、趋化因子介导的信号通路作用等。显著富集差异表达基因KEGG通路分析包括Endocytosis和Hepatitis B的相关信号通路。PPI筛选出EPS15、STAT4、CCL2、SUN2、SEC24C、SEC31A、GOLGB1、F2R,RAB12和PTK2B等关键基因。成功筛选出核心差异表达基因,为IgAN和MN的诊断和鉴别提供简便、可靠的依据补充,甚至提供治疗的新靶点。     

Network pharmacology,molecular docking integrated surface plasmon resonance technology reveals the mechanism of Toujie Quwen Granules against coronavirus disease 2019 pneumonia

BackgroundThe Coronavirus disease 2019 pneumonia broke out in 2019 (COVID-19) and spread rapidly, which causes serious harm to the health of people and a huge economic burden around the world.PurposeIn this study, the network pharmacology, molecular docking and surface plasmon resonance technology (SPR) were used to explore the potential compounds and interaction mechanism in the Toujie Quwen Granules (TQG) for the treatment of coronavirus pneumonia 2019.Study designThe chemical constituents and compound targets of Lonicerae Japonicae Flos, Pseudostellariae Radix, Artemisia Annua L, Peucedani Radix, Forsythiae Fructus, Scutellariae Radix, Hedysarum Multijugum Maxim, Isatidis Folium, Radix Bupleuri, Fritiliariae Irrhosae Bulbus, Cicadae Periostracum, Poria Cocos Wolf, Pseudobulbus Cremastrae Seu Pleiones, Mume Fructus, Figwort Root and Fritillariae Thunbrgii Bulbus in TQG were searched. The target name was translated to gene name using the UniProt database and then the Chinese medicine-compound-target network was constructed. Protein-protein interaction network (PPI), Gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the core targets were performed in the Metascape to predict its mechanism. The top 34 compounds in the Chinese medicine-compound-target network were docked with SARS-CoV-2 3CL enzyme and SARS-­CoV­-2 RNA-dependent RNA polymerase (RdRp) and then the 13 compounds with lowest affinity score were docked with angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 Spike protein and interleukin 6 to explore its interaction mechanism. Lastly, SPR experiments were done using the quercetin, astragaloside IV, rutin and isoquercitrin, which were screened from the Chinese medicine-compound-target network and molecular docking.ResultsThe Chinese medicine-compound-target network includes 16 medicinal materials, 111 compounds and 298 targets, in which the degree of PTGS2, TNF and IL­6 is higher compared with other targets and which are the disease target exactly. The result of GO function enrichment analysis included the response to the molecule of bacterial origin, positive regulation of cell death, apoptotic signaling pathway, cytokine-mediated signaling pathway, cytokine receptor binding and so on. KEGG pathway analysis enrichment revealed two pathways: signaling pathway­ IL-17 and signaling pathway­ TNF. The result of molecular docking showed that the affinity score of compounds including quercetin, isoquercitrin, astragaloside IV and rutin is higher than other compounds. In addition, the SPR experiments revealed that the quercetin and isoquercitrin were combined with SARS-CoV-2 Spike protein rather than Angiotensin-converting enzyme 2, while astragaloside IV and rutin were combined with ACE2 rather than SARS-CoV-2 Spike protein.ConclusionTQG may have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation related targets and pathways, in the way of multi-component, multi-target and multi-pathway.     

基于网络药理学的益母草治疗产后腹痛的潜在分子机制研究

本文通过网络药理学方法探讨益母草治疗产后腹痛的潜在分子机制。首先根据TCMSP数据库和文献挖掘益母草的活性成分,在TCMSP、Swiss Target Prediction、Similarity ensemble approach平台上检索活性成分靶点,在OMIM、GeneCards上检索产后腹痛靶点,得到益母草-产后腹痛交集靶点。利用STRING数据库构建蛋白互作(PPI)网络,接着利用Cytoscape软件对PPI网络进行拓扑分析,并对拓扑分析筛选出的核心靶点进行基因本体论(Gene Ontology,GO)分析和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路分析。最后利用免疫组化实验验证益母草对流产大鼠模型子宫组织中PGF2αR、MMP9、TIMP1、VEGFA、VEGFR2蛋白表达水平的影响。最终得到益母草活性成分10种,与产后腹痛相关靶点144个;通过PPI网络分析筛选出118个靶点,进一步拓扑分析后得到98个节点;然后对这98个节点进行GO和KEGG注释。GO分析得到1151个生物过程(BP)条目,97个细胞组成(CC)条目,122个分子功能(MF)条目;KEGG分析得到41条通路,主要涉及雌激素、PI3K-Akt、MAPK、HIF-1信号通路等。最后免疫组化实验证明益母草可显著抑制流产模型大鼠子宫组织中PGF2αR、MMP9蛋白上调和TIMP1、VEGFR2蛋白下调。本研究通过网络药理学和免疫组化实验验证,显示益母草治疗产后腹痛是多成分、多靶点、多途径相互作用的结果,为益母草的临床应用提供了一定的理论依据。     

Network Pharmacology and Molecular Docking Reveal the Mechanism of Corydalis saxicola Bunting Total Alkaloids in Treating Radiation-Induced Oral Mucositis

The study aims to explore the effect and mechanism of total alkaloids of Corydalis saxicola Bunting (CSBTA) in the treatment of radiation induced oral mucositis (RIOM) through network pharmacology and molecular docking. The components and corresponding targets of Corydalis saxicola Bunting were screened by literature review. RIOM related targets were obtained in GeneCards. Cytoscape software was used to construct the component-target-pathway network. Protein-Protein Interaction (PPI) networks was constructed by String database. GO and KEGG enrichment analyses were performed by Metascape. AutoDock Vina 4.2 software was used for molecular docking. There were 26 components of CSBTA targeting 61 genes related to RIOM. Through Cytoscape and PPI analysis, 15 core target genes of CSBTA for treating RIOM were identified. GO functional analysis indicated that CSBTA might play a role through kinase binding and protein kinase activation. KEGG pathway analysis showed that the core targets of CSBTA were mainly focused on cancer and reactive oxygen species (ROS) pathway. The results of molecular docking showed that CSBTA had strong binding energy with target protein including SRC, AKT and EGFR. The study demonstrates that CSBTA may treat RIOM by affecting SRC, AKT and EGFR through ROS pathway.     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.     

Exploring the mechanism of ellagic acid against gastric cancer based on bioinformatics analysis and network pharmacology

Ellagic acid (EA) is a natural polyphenolic compound. Recent studies have shown that EA has potential anticancer properties against gastric cancer (GC). This study aims to reveal the potential targets and mechanisms of EA against GC. This study adopted methods of bioinformatics analysis and network pharmacology, including the weighted gene co-expression network analysis (WGCNA), construction of protein–protein interaction (PPI) network, receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival curve analysis, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, molecular docking and molecular dynamics simulations (MDS). A total of 540 EA targets were obtained. Through WGCNA, we obtained a total of 2914 GC clinical module genes, combined with the disease database for screening, a total of 606 GC-related targets and 79 intersection targets of EA and GC were obtained by constructing Venn diagram. PPI network was constructed to identify 14 core candidate targets; TP53, JUN, CASP3, HSP90AA1, VEGFA, HRAS, CDH1, MAPK3, CDKN1A, SRC, CYCS, BCL2L1 and CDK4 were identified as the key targets of EA regulation of GC by ROC and KM curve analysis. The enrichment analysis of GO and KEGG pathways of key targets was performed, and they were mainly enriched in p53 signalling pathway, PI3K-Akt signalling pathway. The results of molecular docking and MDS showed that EA could effectively bind to 13 key targets to form stable protein–ligand complexes. This study revealed the key targets and molecular mechanisms of EA against GC and provided a theoretical basis for further study of the pharmacological mechanism of EA against GC.     

Effect of dapagliflozin on diabetic patients with cardiovascular disease via MAPK signalling pathway

Clinical studies have shown that dapagliflozin can reduce cardiovascular outcome in patients with type 2 diabetes mellitus (T2DM), but the exact mechanism is unclear. In this study, we used the molecular docking and network pharmacology methods to explore the potential mechanism of dapagliflozin on T2DM complicated with cardiovascular diseases (CVD). Dapagliflozin's potential targets were predicted via the Swiss Target Prediction platform. The pathogenic targets of T2DM and CVD were screened by the Online Mendelian Inheritance in Man (OMIM) and Gene Cards databases. The common targets of dapagliflozin, T2DM and CVD were used to establish a protein-protein interaction (PPI) network; the potential protein functional modules in the PPI network were found out by MCODE. Metascape tool was used for Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis. A potential protein functional module with the best score was obtained from the PPI network and 9 targets in the protein functional module all showed good binding properties when docking with dapagliflozin. The results of KEGG pathway enrichment analysis showed that the underlying mechanism mainly involved AGE-RAGE signalling pathway in diabetic complications, TNF signalling pathway and MAPK signalling pathway. Significantly, the MAPK signalling pathway was considered as the key pathway. In conclusion, we speculated that dapagliflozin played a therapeutic role in T2DM complicated with CVD mainly through MAPK signalling pathway. This study preliminarily reveals the possible mechanism of dapagliflozin in the treatment of T2DM complicated with CVD and provides a theoretical basis for future clinical research.