2023 年3 期 第31 卷
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基于生物信息学分析StanfordA型主动脉夹层和高血压的共同差异表达基因
CommonDifferentiallyExpressedGenesbetweenStanfordTypeAAorticDissectionandHypertensionBasedonBioinformatics
作者:王雨桐,范家铭,杨秭莹,蒋廷波
- 单位:
- 1.苏州大学附属第一医院心血管内科2.苏州大学心血管病研究所3.苏州大学附属第一医院心脏大血管外科
- Units:
- 1.CardiovascularDepartment,FirstAffiliatedHospitalofSoochowUniversity,Suzhou215000,China2.InstituteforCardiovascularScienceofSoochowUniversity,Suzhou215000,China3.DepartmentofCardiacSurgery,FirstAffiliatedHospitalofSoochowUniversity,Suzhou215000,China
- 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;
- Keywords:
- Aneurysm,dissecting;Aorticdissection;Hypertension;Differentiallyexpressedgenes;Computationalbiology
- CLC:
- R543.16R544.1
- DOI:
- 10.12114/j.issn.1008-5971.2023.00.085
- Funds:
- 国家自然科学基金资助项目( 91839101?)
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GEO2R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。
Abstract:
ObjectiveToanalyzethecommondifferentiallyexpressedgenes(DEGs)betweenStanfordtypeAaorticdissection(AD)andhypertensionbasedonbioinformatics.MethodsThisstudytimewasfromJune2021toJune2022.TheGSE52093datasetandtheGSE76845datasetwerescreenedintheGeneExpressionOmnibus(GEO)databaseoftheNationalCenterforBiotechnologyInformationintheUnitedStates.TheonlineeditingtoolGEO2RwasusedtoscreentheDEGsoftheGSE52093datasetandtheGSE76845dataset,andtheVeenmapwasdrawntofindthecommonDEGsoftheGSE52093datasetandtheGSE76845dataset.TheonlinedatabaseDAVIDwasusedtoperformGOfunctionenrichmentanalysisonDEGsofGSE52093datasetandGSE76845datasetandtheircommonDEGs.TheonlinedatabaseKOBAS3.0wasusedtoperformKEGGpathwayenrichmentanalysisonthecommonDEGsofGSE52093datasetandGSE76845dataset.TheproteininteractionnetworkdiagramofthecommonDEGsoftheGSE52093datasetandtheGSE76845datasetwasimportedintoCytoscapesoftware,andHubgeneswerescreenedaccordingtothefivetopologicalanalysismethods,includingdensityofmaximumneighborhoodcomponent(DMNC),edge percolatedcomponent(EPC),maximalcliquecentrality(MCC),stressanddegree,andthenthecommonHub genesofthefivetopologicalanalysisresultswerescreenedbyVeenmap.ResultsTheresultsofVennmapanalysisshowedthattherewere16commmonDEGsoftheGSE52093datasetandtheGSE76845dataset,including8jointlyup-regulatedDEGsand8down-regulatedDEGs.GOfunctionalenrichmentanalysisresultsshowedthatthecommonDEGsofGSE52093datasetandGSE76845datasetweremainlyenrichedinossification,negativeregulationofcelldifferentiation,hormoneregulationandnegativeregulationofcellcycle.TheresultsofKEGGpathwayenrichmentanalysisshowedthatthecommonDEGsofGSE52093datasetandGSE76845datasetweremainlyenrichedinfolate-onecarbonunitcycle,folatemetabolism,pyrimidinemetabolism,cholesterolmetabolismandHippopathway.SevencommonHubgeneswerescreenedoutbyVennmap,whichwereMAD2L1,TYMS,SPP1,ACTG2,MARK1,LATS2andCASQ2,inwhichMAD2L1,TYMSandSPP1weredown-regulatedHubgenes,andACTG2,MARK1,LATS2andCASQ2wereup-regulatedHubgenes.ConclusionInthisstudy,sevencommonhubgenesofStanfordtypeAADandhypertensionarescreenedout,whichareMAD2L1,TYMS,SPP1,ACTG2,MARK1,LATS2andCASQ2,respectively,andup-regulatedHubgenesACTG2,MARK1,LATS2andCASQ2maybebiologicalpredictorsandpotentialdrugtargetsforADpatientswithhypertension.
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