Post-marketing Evaluation Study Of Danshen Injection In The Treatment Of Acute Coronary Syndrome Based On Integrated Big Data

Background:According to the statistics of the World Health Organization,the mortality rate of cardiovascular diseases ranks first among non-communicable diseases in the world.In China,with the rapid economic development and aging of the population,acute coronary syndrome(ACS)has become one of the leading reasons of death caused by cardiovascular diseases in the total number of urban and rural residents.Based on the diagnostic criteria,ACS is divided into acute myocardial infarction(AMI)and unstable angina(UA).In clinical practice,conventional Western medicine therapy is often used to treat ACS,but it also has certain shortcomings.For example,many patients have complications after using percutaneous coronary intervention,which increases the rehospitalization rate of ACS patients.In order to enhance the clinical efficacy,reduce complications and improve the prognosis of patients,adjuvant treatment of traditional Chinese medicine is particularly important.In recent years,Danshen class injections(DSCIs),which have the effects of activating blood to remove blood stasis,and nourishing the heart,have been widely used in the treatment of ACS.Therefore,this study adopted network Meta-analysis,bioinformatics and network pharmacology methods to explore the clinical evaluation and mechanism of DSCIs against ACS.Objective:In this study,the method of network Meta-analysis has been used to select the preferable options among different DSCIs for treating ACS,which provides a reference for clinical rational drug use and the formulation of relevant treatment guidelines.At the same time,using the methods of bioinformatics and network pharmacology,scientifically predict and identify the key differential genes that affect the occurrence and development of ACS,and systematically reveal the close relationship between the "key components","core targets" and "important pathways" of the advantage varieties of DSCIs in the treatment of ACS.It is helpful to provide a reference for further exploration of the mechanism of action of traditional Chinese medicine for treating ACS.Methods:1.Network Meta-analysisThe randomized controlled trials of DSCIs in the treatment of ACS were systematically searched in CNKI,VIP database,Wanfang database,SinoMed,Cochrane Library,PubMed and Embase.Subsequently,according to the PICOS principle,the inclusion and exclusion criteria were formulated and the literature was screened.In addition,data extraction,data aggregation and risk evaluation were carried out on the included studies.What's more,WingBUGS 1.4.3 and Stata 13.0 software were used to analyze the data and draw the network diagram,publication bias detection diagram,consistency test diagram and multi-dimensional clustering analysis diagram to obtain the preferable varieties of DSCIs in the treatment of ACS.2.Bioinformatics analysisThe ACS-related data set was retrieved through GEO database,and the "Limma" package in R 3.6.1 software was used to screen differentially expressed genes(DEGs).In addition,the protein-protein interaction(PPI)information of differential genes was obtained through the STRING database,and imported into Cytoscape 3.7.1 software to construct a PPI network map,and the MCODE and CytoHubba plug-ins were used to perform module analysis and determine key genes respectively.Subsequently,the "ClusterProfiler" and "GOplot" packages in the R software were used for enrichment analysis of DEGs.3.Network pharmacology analysisThe chemical components related to Danhong injection were searched through PubMed and CNKI databases,and the structural information of candidate compounds was searched through PubChem database.The 2D structures of candidate compounds were drawn by ChemDraw software.The compound targets were predicted by SuperPred,Swiss TargetPrediction and BATMAN-TCM database.ACS-related targets were collected from the DisGeNET,DigSee,TTD,OMIM,and GEO databases.Then,GO and KEGG pathways enrichment analysis were performed with"ClusterProfiler" package in R software.The STRING database was adopted to obtain the relevant protein interaction data information.Based on the above data,Cytoscape 3.7.1 software was used to construct related network.Finally,molecular docking simulation was conducted by AutoDock Tools,AutoDock Vina and PyMol software to observe the binding activity of key targets and corresponding compounds.Results:1.The results of network Meta-analysis of DSCIs in the treatment of ACSA total of 53 studies were included in this study,including 6401 patients.And the types of DSCIs involved were:Danshen injection(DS),Fufang Danshen injection(FFDS),Dansenduofensuanyan injection(DSDFSY),Danhong injection(DH),Danshenchuanxiongqin injection(DSCXQ),Danshen Salvianolic Acids injection(DSSA),Guanxinning injection(GXN)and Sodium Tanshinone IIA Sulfonate injection(STS).All DSCIs combined with western medicine(WM)had superior effects compared with WM alone among the overall outcomes.The cluster analysis results revealed that DS+WM and DH+WM were the most likely to be the best intervention.At the same time.by comprehensively considering the results of primary and secondary outcome indicators.ranking of intervention measures and multidimensional cluster analysis.DH was considered as the dominant variety in the treatment of ACS for further study of its mechanism.In the aspect of safety,the reported adverse reactions mainly include allergic rash,vomiting,drowsiness,flushing,headache and mild gastrointestinal reactions.2.The results of key DEGs in AMI based on bioinformatics analysisAfter strict screening,the AMI chip data set(GSE66360)was downloaded from the GEO database.A total of 351 DEGs,including 289 down-regulated genes and 62 up-regulated genes,were identified after analysis.Subsequently.through enrichment analysis,PPI.module analysis,correlation analysis and expression level analysis,10 key genes(IL1B,CXCL1,CXCL8,TNF,FPR2,JUN,PPBP,MMP9,TLR2 and FCER1)that may be related to the occurrence and development of AMI were obtained,all of which were up-regulated genes and showed positive correlation.GO enrichment analysis showed that the key genes were mainly related to neutrophils and cytokines.KEGG enrichment analysis indicated that the key genes were mainly concentrated in NF-?B signaling pathway,cytokine-cytokine receptor interaction,TNF signaling pathway and IL-17 signaling pathway.3.The results of key DEGs in AMI and UA based on bioinformatics analysisAfter rigorous screening,chip data sets(GSE29111,GSE60993)containing both AMI and UA were downloaded from the GEO database.Due to the differences in the annotation platform,blood collection time and patient source of the two chips,the two data sets were analyzed respectively.A total of 242 DEGs were obtained from GSE29111,including 128 up-regulated genes and 114 down-regulated genes.A total of 44 differential genes were identified from GSE60993,including 4 up-regulated genes and 40 down-regulated genes.In addition,the 10 key genes obtained from GSE29111 were:BDKRB1,BDKRB2,CCL25,HRH1,KISS1,NPBWR1,GRPR,TACR3,HTR2B and ORM1.GO enrichment analysis showed that the key genes were mainly related to inflammatory response,G-protein coupled receptor and plasma membrane.KEGG enrichment analysis indicated that the key genes were mainly concentrated in neuroactive ligand-receptor interaction,calcium signaling pathways and inflammatory mediator regulation of TRP channels pathways.The 10 key genes involved in GSE60993 included IFI44,FFAR2,GNG10,CXCR2,MCEMP1,FPR2,CLEC4D,IFIT3,RSAD2 and PLSCR1.GO enrichment analysis showed that the key genes were mainly related to neutrophils activation,immune response,inflammatory response,granules,plasma membrane and cytokines.KEGG enrichment analysis indicated that the key genes were mainly enriched in signal transduction,such as the cytokine-cytokine receptor interaction signaling pathway.4.The results of network pharmacology analysis on the mechanism of Danhong injection in the treatment of AMIThrough network construction,enrichment analysis and molecular docking,a total of 10 key targets were obtained:MAPK1,MAPK3,PIK3CA,MAPK8,JUN,TNF,EGFR,STAT3,SRC,APP.And these key targets had good binding activity with caffeic acid,ferulic acid and rosmarinic acid.GO enrichment analysis showed that key targets are mainly enriched in regulating blood vessel size,blood circulation,cell membrane,G protein coupling and enzyme activity.KEGG enrichment analysis results indicated that the key targets were mainly involved in the AGE-RAGE signaling pathway in diabetic complications,Chagas disease pathway and IL-17 signaling pathway.5.The results of network pharmacology analysis on the mechanism of Danhong injection in the treatment of UAThrough network construction,enrichment analysis and molecular docking,4 key targets were obtained:TNF,TLR4,NFKB1 and SERPINE1,and they have good binding activity with rosmarinic acid and caffeic acid.GO enrichment analysis showed that key targets were mainly related to coagulation,hemostasis,cell membrane area,platelet ? particles and peptidase activity.KEGG enrichment analysis results indicated that key genes are mainly involved in NF?B signaling pathway,TNF signaling pathway,complement and coagulation cascades,toll-like receptor signaling pathway and IL-17 signaling pathway.Conclusion:In general,based on the results of the network meta-analysis,the current evidence indicates that DSCIs combined with WM might have a more beneficial effect on ACS patients than WM alone,and it played a certain auxiliary role in improving the total clinical effective rate,alleviating adverse reactions and reducing the levels of hs-CRP,CRP,IL-6 and FIB.Through the comprehensive application of bioinformatics and network pharmacology methods,this study revealed the key genes and potentially important pathways related to the occurrence and development of ACS.Meanwhile,the complex mechanism of "multi-component,multitarget and multi-pathway" of Danhong injection in the treatment of ACS was explained systematically.However,because each patient responds differently to injections,it is still necessary to choose the best personalized treatment programs based on the patient's condition,physician's medication experience and high-quality evidence-based medical evidence.