基于GEO数据库芯片的心肌梗死标志物的筛选与生物信息学分析
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摘要
目的:对首次急性心肌梗死(acute myocardial infarction,AMI)患者基因芯片数据进行生物信息学分析,寻找表达特征基因谱。方法:利用GEO数据库中高通量基因芯片数据筛选出AMI信息的芯片。采用GO基因功能注释并进行蛋白质相互作用网络可视化分析。结果:经过数据分析,这些差异性表达的基因被富集到不同的生物学过程或分子功能的子集中。结论:通过对GEO数据库中AMI的表达数据分析研究而选出的22个差异表达基因,可为该疾病的早期诊断治疗和靶向药物的开发提供重要理论依据。
Objective: To perform bioinformatics analysis on the gene chip data of patients with first acute myocardial infarction(AMI) to find the gene expression profile. Methods: We used the high-throughput gene chip data in the GEO database to screen out the AMI information chip. The GO gene function was used for annotation and visual analysis of the protein interaction network. Results: After data analysis, these differentially expressed genes were enriched in subsets of different biological processes or molecular functions. Conclusion: Twenty-two differentially expressed genes were selected by analyzing the expression data of AMI in the GEO database, which can provide an important theoretical basis for the early diagnosis and treatment of this disease and the development of targeted drugs.
Objective: To perform bioinformatics analysis on the gene chip data of patients with first acute myocardial infarction(AMI) to find the gene expression profile. Methods: We used the high-throughput gene chip data in the GEO database to screen out the AMI information chip. The GO gene function was used for annotation and visual analysis of the protein interaction network. Results: After data analysis, these differentially expressed genes were enriched in subsets of different biological processes or molecular functions. Conclusion: Twenty-two differentially expressed genes were selected by analyzing the expression data of AMI in the GEO database, which can provide an important theoretical basis for the early diagnosis and treatment of this disease and the development of targeted drugs.
引文
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24 .Wettinger SB, Doggen CJ, Spek CA, et al. High throughput mRNA profiling highlights associations between myocardial infarction and aberrant expression of inflammatory molecules in blood cells[J]. Blood,2005, 105(5):2000-2006.
25 .张新春,郑景辉,莫云秋,等.基于GEO数据库芯片的早期ST段抬高性心肌梗死标志物的筛选及生物信息学分析[J].当代医学, 2018, 24(12):1-6.ZHANG Xinchun, ZHENG Jinghui, MO Yunqiu, et al. GEO databasebasedbioinformaticsstudyongenechipofmarkersofearlyST segment elevation myocardial infarction[J]. Contemporary Medicine,2018, 24(12):1-6.
2 .Go AS, Mozaffarian D, Roger VL, et al. Executive summary:heart disease and stroke statistics--2013 update:a report from the American Heart Association[J]. Circulation, 2013, 127(1):143-152.
3 .GoAS,MozaffarianD,RogerVL,etal.Heartdiseaseandstroke stati stics--2013update:arepor tf romtheA mer icanHear t Association[J]. Circulation, 2013, 127(1):e6-e245.
4 .Antman EM, Cohen M, Bernink PJ, et al. The TIMI risk score for unstable angina/non-ST elevation MI:a method for prognostication and therapeutic decision making[J]. JAMA, 2000, 284(7):835-842.
5 .LawMR,WattHC,WaldNJ.Theunderlyingriskofdeathafter myocardial infarction in the absence of treatment[J]. Arch Intern Med,2002, 162(21):2405-2410.
6 .Ge Y, Wang TJ. Identifying novel biomarkers for cardiovascular disease risk prediction[J]. J Intern Med, 2012, 272(5):430-439.
7 .Bj?rkegren JLM, Kovacic JC, Dudley JT, et al. Genome-wide significant loci:howimportantarethey?Systemsgeneticstounderstand heritability of coronary artery disease and other common complex disorders[J]. J Am Coll Cardiol, 2015, 65(8):830-845.
8 .Lettre G. Rare and low-frequency variants in human common diseases and other complex traits[J]. J Med Genet, 2014, 51(11):705-714.
9 .GersztenR E,WangTJ.Thesearchfornewcardiovascular biomarkers[J]. Nature, 2008, 451(7181):949-952.
10 .Pedrotty DM, Morley MP, Cappola TP. Transcriptomic biomarkers of cardiovascular disease[J]. Prog Cardiovasc Dis, 2012, 55(1):64-69.
11 .Beisvag V, Lehre PK, Midelfart H, et al. Aetiology-specific patterns in end-stage heart failure patients identified by functional annotation and classification of microarray data[J]. Eur J Heart Fail, 2006, 8(4):381-389.
12 .KittlesonMM,YeSQ,IrizarryRA,etal.Identificationofagene expression profile that differentiates between ischemic and nonischemic cardiomyopathy[J]. Circulation, 2004, 110(22):3444-3451.
13 .BurkeGM,GenuardiM,ShappellH,etal.Temporalassociations between smoking and cardiovascular disease, 1971 to 2006(from the Framingham Heart Study)[J]. Am J Cardiol, 2017, 120(10):1787-1791.
14 .TiburcyM,ZimmermannWH.Modelingmyocardialgrowthand hypertrophy in engineered heart muscle[J]. Trends Cardiovasc Med,2014, 24(1):7-13.
15 .Carvalho E, Verma P, Hourigan K, et al. Myocardial infarction:stem cell transplantation for cardiac regeneration[J]. Regen Med, 2015,10(8):1025-1043.
16.Liehn EA, Postea O, Curaj A, et al. Repair after myocardial infarction,between fantasy and reality:the role of chemokines[J]. J Am Coll Cardiol, 2011, 58(23):2357-2362.
17 .Ohlin AK, Morser J, Ohlin H. Soluble thrombomodulin antigen in plasma is increased in patients with acute myocardial infarction treated with thrombolytic therapy[J]. Thromb Res, 1996, 82(4):313-322.
18 .张文,陈文强,朱斌,等.冠心病患者外周血内皮细胞膜微粒、辅助性T细胞细胞因子水平变化及临床意义[J].国际检验医学杂志, 2017, 38(2):245-247.ZHANG Wen, CHEN Wenqiang, ZHU Bin, et al. Changes and clinical significance of membrane microparticles and helper T cell cytokines in peripheral blood endothelial cells of patients with coronary heart disease[J].International Journal of Laboratory Medicine, 2017, 38(2):245-247.
19 .YanW,ZhouL,WenS,etal.Differentiallossofnaturalkillercell activity in patients with acute myocardial infarction and stable angina pectoris[J]. Int J Clin Exp Pathol, 2015, 8(11):14667-14675.
20 .ZhangS,LiuW,LiuX,etal.Biomarkersidentificationforacute myocardialinfarctiondetectionviaweightedgeneco-expression network analysis[J]. Medicine(Baltimore), 2017, 96(47):e8375.
21 .Chen J, Yu L, Zhang S, et al. Network analysis-based approach for exploringthepotentialdiagnosticbiomarkersofacutemyocardial infarction[J]. Front Physiol, 2016, 7:615.
22 .JoehanesR,YingS,HuanT,etal.Geneexpressionsignaturesof coronaryheartdisease[J].ArteriosclerThrombVascBiol,2013,33(6):1418-1426.
23 .Calvano SE, Xiao W, Richards DR, et al. A network-based analysis of systemic inflammation in humans[J]. Nature, 2005, 437(7061):1032-1037.
24 .Wettinger SB, Doggen CJ, Spek CA, et al. High throughput mRNA profiling highlights associations between myocardial infarction and aberrant expression of inflammatory molecules in blood cells[J]. Blood,2005, 105(5):2000-2006.
25 .张新春,郑景辉,莫云秋,等.基于GEO数据库芯片的早期ST段抬高性心肌梗死标志物的筛选及生物信息学分析[J].当代医学, 2018, 24(12):1-6.ZHANG Xinchun, ZHENG Jinghui, MO Yunqiu, et al. GEO databasebasedbioinformaticsstudyongenechipofmarkersofearlyST segment elevation myocardial infarction[J]. Contemporary Medicine,2018, 24(12):1-6.