摘要
目的基于生物信息学分析Stanford A型主动脉夹层(AD)和高血压的共同差异表达基因(DEGs)。方法本研究时间为2021年6月至2022年6月。在美国国家生物技术信息中心的基因表达综合数据库(GEO)筛选出GSE52093数据集和GSE76845数据集,采用在线编辑工具GEO_(2)R筛选GSE52093数据集和GSE76845数据集的DEGs,绘制韦恩图以分析GSE52093数据集和GSE76845数据集的共同DEGs。利用在线数据库DAVID对GSE52093数据集、GSE76845数据集的DEGs及二者共同DEGs进行GO功能富集分析,利用在线数据库KOBAS 3.0对GSE52093数据集和GSE76845数据集的共同DEGs进行KEGG通路富集分析。将GSE52093数据集和GSE76845数据集的共同DEGs的蛋白质相互作用网络图导入Cytoscape软件,并根据最大领域组件密度(DMNC)、边缘渗透组件(EPC)、最大集团中心度(MCC)、应力、度五种拓扑分析方法筛选Hub基因,然后通过韦恩图筛选五种拓扑分析结果的共同Hub基因。结果韦恩图分析结果显示,GSE52093数据集与GSE76845数据集的共同DEGs有16个,其中共同上调DEGs 8个,共同下调DEGs 8个。GO功能富集分析结果显示,GSE52093数据集和GSE76845数据集的共同DEGs主要富集在骨化、细胞分化的负调控、激素调节和细胞周期的负调控。KEGG通路富集分析结果显示,GSE52093数据集和GSE76845数据集的共同DEGs主要富集在叶酸-一碳单位循环、叶酸代谢、嘧啶代谢、胆固醇代谢和Hippo通路。韦恩图筛选出7个共同Hub基因,分别是MAD2L1、TYMS、SPP1、ACTG2、MARK1、LATS2、CASQ2,其中MAD2L1、TYMS、SPP1为下调Hub基因,ACTG2、MARK1、LATS2、CASQ2为上调Hub基因。结论本研究共筛选出7个Stanford A型AD和高血压的共同Hub基因,分别是MAD2L1、TYMS、SPP1、ACTG2、MARK1、LATS2、CASQ2,其中上调的Hub基因ACTG2、MARK1、LATS2、CASQ2可能成为AD伴高血压患者的生物学预测因子和潜在药物作用靶点。
Objective To analyze the common differentially expressed genes(DEGs)between Stanford type A aortic dissection(AD)and hypertension based on bioinformatics.Methods This study time was from June 2021 to June 2022.The GSE52093 dataset and the GSE76845 dataset were screened in the Gene Expression Omnibus(GEO)database of the National Center for Biotechnology Information in the United States.The online editing tool GEO_(2)R was used to screen the DEGs of the GSE52093 dataset and the GSE76845 dataset,and the Veen map was drawn to find the common DEGs of the GSE52093 dataset and the GSE76845 dataset.The online database DAVID was used to perform GO function enrichment analysis on DEGs of GSE52093 dataset and GSE76845 dataset and their common DEGs.The online database KOBAS 3.0 was used to perform KEGG pathway enrichment analysis on the common DEGs of GSE52093 dataset and GSE76845 dataset.The protein interaction network diagram of the common DEGs of the GSE52093 dataset and the GSE76845 dataset was imported into Cytoscape software,and Hub genes were screened according to the five topological analysis methods,including density of maximum neighborhood component(DMNC),edge percolated component(EPC),maximal clique centrality(MCC),stress and degree,and then the common Hub genes of the five topological analysis results were screened by Veen map.Results The results of Venn map analysis showed that there were 16 commmon DEGs of the GSE52093 dataset and the GSE76845 dataset,including 8 jointly up-regulated DEGs and 8 down-regulated DEGs.GO functional enrichment analysis results showed that the common DEGs of GSE52093 dataset and GSE76845 dataset were mainly enriched in ossification,negative regulation of cell differentiation,hormone regulation and negative regulation of cell cycle.The results of KEGG pathway enrichment analysis showed that the common DEGs of GSE52093 dataset and GSE76845 dataset were mainly enriched in folate-one carbon unit cycle,folate metabolism,pyrimidine metabolism,cholesterol metabolism and Hippo pathway.Seven common Hub genes were screened out by Venn map,which were MAD2L1,TYMS,SPP1,ACTG2,MARK1,LATS2 and CASQ2,in which MAD2L1,TYMS and SPP1 were down-regulated Hub genes,and ACTG2,MARK1,LATS2 and CASQ2 were up-regulated Hub genes.Conclusion In this study,seven common hub genes of Stanford type A AD and hypertension are screened out,which are MAD2L1,TYMS,SPP1,ACTG2,MARK1,LATS2 and CASQ2,respectively,and up-regulated Hub genes ACTG2,MARK1,LATS2 and CASQ2 may be biological predictors and potential drug targets for AD patients with hypertension.
作者
王雨桐
范家铭
杨秭莹
蒋廷波
WANG Yutong;FAN Jiaming;YANG Ziying;JIANG Tingbo(Cardiovascular Department,First Affiliated Hospital of Soochow University,Suzhou 215000,China;Institute for Cardiovascular Science of Soochow University,Suzhou 215000,China;Department of Cardiac Surgery,First Affiliated Hospital of Soochow University,Suzhou 215000,China)
出处
《实用心脑肺血管病杂志》
2023年第3期81-86,共6页
Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease
基金
国家自然科学基金资助项目(91839101)。
关键词
动脉瘤
夹层
主动脉夹层
高血压
差异表达基因
计算生物学
Aneurysm,dissecting
Aortic dissection
Hypertension
Differentially expressed genes
Computational biology