Bioinformatics Analysis Of Atherosclerotic Biomarkers

Research Purpose: In this study,atherosclerosis(AS)patients with gene chip data were analyzed to find potential genes during the development of AS.The purpose of this study is to integrate and analyze known data through bioinformatics technology,analyze differentially expressed genes and analyze their biological functions and pathways through R language.Through the analysis and screening of protein interaction network,24 differential genes with high scores were obtained,and then the accuracy of the conclusion of this study was verified by searching the articles,so as to find the potential genes in the process of AS development.Research methods: In this study,"Atherosclerosis" was used as a key word to search the National Biotechnology Information Center(NCBI)GEO database(www.ncbi.nlm.nih.gov/geo)to search for published atherosclerosis gene chip data sets,The high-throughput gene chip data GSE43292 and GSE28829 in the GEO database were downloaded and included 46 cases of carotid atherosclerotic plaque tissue.Subsequently,the R language limma package was used for data standardization,and the differential genes in the data were screened using log fold change>1.5,adjust<0.05 as the criteria.The differential gene function analysis was performed using the DAVID online database,and the differential genes were analyzed using the KEGG database.Path analysis,and visualized with P<0.05 as the standard.Then use STRING database for protein interaction network analysis and select the main protein interaction network with a comprehensive score greater than 0.4,and import the obtained interaction network into Cytoscape software [9-10]] for further analysis.Using Cytoscape software to draw the protein interaction network,and using the MCODE module to determine the network with MCODE scores>5,degree cut-off=2,node score cut-off=0.2,Max depth=100,k-score=2 And display important areas.Afterwards,the accuracy and authenticity of the conclusion of this study are verified by retrieving articles.Research results: After standardizing the original data,this study obtained the standardized box distribution.Through cluster analysis of standardized data,a heat map was drawn and the phenomenon of gene expression clustering was found in the heat map.Through screening of differential genes,1486 up-regulated genes and 1333 down-regulated genes were obtained and displayed with volcano graphs.The differentially expressed genes obtained were analyzed in the DAVID online database.Enriched on the cell surface,outside plasma membrane,extracellular body,extracellular area,cell matrix,the main biological process is enriched in immunoglobulin receptor binding,immune response,inflammatory response,cAMP signaling pathway,cell adhesion pathway,Cyclic body strengthening and tryptophan metabolism pathways,etc.Using STRING database for protein interaction network analysis and importing Cytoscape software to screen out 24 key genes MMP9,CXCR4,FABP4,etc.as potential key genes that may lead to atherosclerosis,by searching articles to verify the accuracy of the conclusion of this study And authenticity,it is finally concluded that MMP9,CXCR4,FABP4 are related to AS.Research conclusions and Significance: This study concluded that MMP9,CXCR4,and FABP4 are associated with atherosclerosis,which may be a marker of atherosclerosis.Finally,24 key genes were identified in this study.Genes other than MMP9,FABP4,and CXCR4 also had high scores and statistically significant P values in this study,thus speculating that they may be related to atherosclerosis..The significance of this research is to apply bioinformatics technology and atherosclerosis to provide algorithmic prediction and data analysis support for the development of molecular diagnosis and treatment of atherosclerosis,so as to help us discover disease earlier and faster.Target genes and further guide clinical diagnosis and treatment.The innovation of this study is the use of bioinformatics methods to study the potential pathogenic genes of atherosclerosis,which is different from the review and Meta analysis.It is a method innovation.This study further standardizes the data obtained and reduces the number of different research genes.Differences in data.