open access

Vol 25, No 3 (2018)
Original articles — Basic science and experimental cardiology
Published online: 2017-09-06
Get Citation

Screening hub genes in coronary artery disease based on integrated analysis

Fei Long, Ling Wang, Lei Yang, Zhou Ji, YaGuang Hu
DOI: 10.5603/CJ.a2017.0106
·
Pubmed: 28980286
·
Cardiol J 2018;25(3):403-411.

open access

Vol 25, No 3 (2018)
Original articles — Basic science and experimental cardiology
Published online: 2017-09-06

Abstract

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Identifying key pathogenic genes benefits the understanding molecular mechanism of CAD.

Methods: In this study, 5 microarray data sets from the blood sample of 312 CADs and 277 healthy controls were downloaded. Limma and metaMA packages were used to identify differentially expressed genes. The functional enrichment analysis of differentially expressed genes was further performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Additionally, protein–protein interac­tion and transcript factors-target networks were performed based on top 10 up- and down-regulated differentially expressed genes to further study the biological function. Last, real-time quantitative poly­merase chain reaction (RT-qPCR) was used to validate the integrated analysis result.

Results: A total of 528 differentially expressed genes were obtained. All differentially expressed genes were significantly involved in signal transduction and the MAPK signaling pathway. Among MAPK signaling pathway, IL1R2, ARRB2 and PRKX were associated with CAD. Furthermore, there were 4 common differentially expressed genes including PLAUR, HSPH1, ZMYND11 and S100A8 in the protein–protein interaction and transcript factors-target networks, which played crucial roles in the development of CAD. In quantitative RT-qPCR, the expression of PRKX, HSPH1 and ZMYND11 was down-regulated and consistent with the integrated analysis.

Conclusions: Identified 7 differentially expressed genes (IL1R2, ARRB2, PRKX, PLAUR, HSPH1, ZMYND11 and S100A8) may play crucial roles in the development of CAD.

Abstract

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Identifying key pathogenic genes benefits the understanding molecular mechanism of CAD.

Methods: In this study, 5 microarray data sets from the blood sample of 312 CADs and 277 healthy controls were downloaded. Limma and metaMA packages were used to identify differentially expressed genes. The functional enrichment analysis of differentially expressed genes was further performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Additionally, protein–protein interac­tion and transcript factors-target networks were performed based on top 10 up- and down-regulated differentially expressed genes to further study the biological function. Last, real-time quantitative poly­merase chain reaction (RT-qPCR) was used to validate the integrated analysis result.

Results: A total of 528 differentially expressed genes were obtained. All differentially expressed genes were significantly involved in signal transduction and the MAPK signaling pathway. Among MAPK signaling pathway, IL1R2, ARRB2 and PRKX were associated with CAD. Furthermore, there were 4 common differentially expressed genes including PLAUR, HSPH1, ZMYND11 and S100A8 in the protein–protein interaction and transcript factors-target networks, which played crucial roles in the development of CAD. In quantitative RT-qPCR, the expression of PRKX, HSPH1 and ZMYND11 was down-regulated and consistent with the integrated analysis.

Conclusions: Identified 7 differentially expressed genes (IL1R2, ARRB2, PRKX, PLAUR, HSPH1, ZMYND11 and S100A8) may play crucial roles in the development of CAD.

Get Citation

Keywords

coronary artery disease, differentially expressed gene, protein–protein interaction, transcript factors

Supplementary Files (2)
Supplementary Table 1
Download
28KB
Suppl. Fig. 1 and Suppl. Fig. 2
Download
4MB
About this article
Title

Screening hub genes in coronary artery disease based on integrated analysis

Journal

Cardiology Journal

Issue

Vol 25, No 3 (2018)

Pages

403-411

Published online

2017-09-06

DOI

10.5603/CJ.a2017.0106

Pubmed

28980286

Bibliographic record

Cardiol J 2018;25(3):403-411.

Keywords

coronary artery disease
differentially expressed gene
protein–protein interaction
transcript factors

Authors

Fei Long
Ling Wang
Lei Yang
Zhou Ji
YaGuang Hu

References (48)
  1. Chen CL, Chen L, Yang WC. The influences of Taiwan's generic grouping price policy on drug prices and expenditures: evidence from analysing the consumption of the three most-used classes of cardiovascular drugs. BMC Public Health. 2008; 8: 118.
  2. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet. 1997; 349(9064): 1498–1504.
  3. Opstad TB, Arnesen H, Pettersen AÅ, et al. The MMP-9 -1562 C/T polymorphism in the presence of metabolic syndrome increases the risk of clinical events in patients with coronary artery disease. PLoS One. 2014; 9(9): e106816.
  4. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004; 364(9438): 937–952.
  5. Deb S, Wijeysundera HC, Ko DT, et al. Coronary artery bypass graft surgery vs percutaneous interventions in coronary revascularization: a systematic review. JAMA. 2013; 310(19): 2086–2095.
  6. Yang Y, Yang W, Huo W, et al. Identification of biomarkers for ischemic cardiomyopathy based on microarray data analysis. Cardiol J. 2017; 24(3): 305–313.
  7. Marot G, Foulley JL, Mayer CD, et al. Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics. 2009; 25(20): 2692–2699.
  8. Wrighton KH, Lin X, Yu PB, et al. Transforming Growth Factor {beta} Can Stimulate Smad1 Phosphorylation Independently of Bone Morphogenic Protein Receptors. J Biol Chem. 2009; 284(15): 9755–9763.
  9. Chatr-Aryamontri A, Oughtred R, Boucher L, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015; 43(Database issue): D470–D478.
  10. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11): 2498–2504.
  11. Stelzl U, Worm U, Lalowski M, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005; 122(6): 957–968.
  12. Zhang Y, Tang W, Peng L, et al. Identification and validation of microRNAs as endogenous controls for quantitative polymerase chain reaction in plasma for stable coronary artery disease. Cardiol J. 2016; 23(6): 694–703.
  13. Libby P. Inflammation in Atherosclerosis. Arterioscler Thromb Vasc Biol. 2012; 32(9): 2045–2051.
  14. Seger R, Krebs EG. The MAPK signaling cascade. FASEB J. 1995; 9(9): 726–735.
  15. Yabluchanskiy A, Ma Y, DeLeon-Pennell KY, et al. Myocardial Infarction Superimposed on Aging: MMP-9 Deletion Promotes M2 Macrophage Polarization. J Gerontol A Biol Sci Med Sci. 2016; 71(4): 475–483.
  16. Park HJ, Noh JiH, Eun JW, et al. Assessment and diagnostic relevance of novel serum biomarkers for early decision of ST-elevation myocardial infarction. Oncotarget. 2015; 6(15): 12970–12983.
  17. Bondar G, Cadeiras M, Wisniewski N, et al. Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure. PLoS One. 2014; 9(12): e115097.
  18. Jickling GC, Xu H, Stamova B, et al. Signatures of cardioembolic and large-vessel ischemic stroke. Ann Neurol. 2010; 68(5): 681–692.
  19. McGeachie M, Ramoni RL, Mychaleckyj JC, et al. Integrative predictive model of coronary artery calcification in atherosclerosis. Circulation. 2009; 120(24): 2448–2454.
  20. Zhang H, Li T, Wu G, et al. Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease. Exp Ther Med. 2014; 7(5): 1151–1154.
  21. Gu S, Su P, Yan J, et al. Comparison of gene expression profiles and related pathways in chronic thromboembolic pulmonary hypertension. Int J Mol Med. 2014; 33(2): 277–300.
  22. Liu Z, Zhou C, Liu Y, et al. The expression levels of plasma micoRNAs in atrial fibrillation patients. PLoS One. 2012; 7(9): e44906.
  23. Liu H, Qin H, Chen Gx, et al. Comparative expression profiles of microRNA in left and right atrial appendages from patients with rheumatic mitral valve disease exhibiting sinus rhythm or atrial fibrillation. J Translat Med. 2014; 12(1): 90.
  24. Devaux Y, Bousquenaud M, Rodius S, et al. Transforming growth factor beta receptor 1 is a new candidate prognostic biomarker after acute myocardial infarction. BMC Med Genomics. 2011; 4: 83.
  25. Ip JE, Wu Y, Huang J, et al. Mesenchymal stem cells use integrin beta1 not CXC chemokine receptor 4 for myocardial migration and engraftment. Mol Biol Cell. 2007; 18(8): 2873–2882.
  26. Masud R, Shameer K, Dhar A, et al. Gene expression profiling of peripheral blood mononuclear cells in the setting of peripheral arterial disease. J Clin Bioinforma. 2012; 2: 6.
  27. Robless PA, Okonko D, Lintott P, et al. Increased platelet aggregation and activation in peripheral arterial disease. Eur J Vasc Endovasc Surg. 2003; 25(1): 16–22.
  28. Huang CC, Lloyd-Jones DM, Guo X, et al. Gene expression variation between African Americans and whites is associated with coronary artery calcification: the multiethnic study of atherosclerosis. Physiol Genomics. 2011; 43(13): 836–843.
  29. Wilensky RL, Shi Yi, Mohler ER, et al. Inhibition of lipoprotein-associated phospholipase A2 reduces complex coronary atherosclerotic plaque development. Nat Med. 2008; 14(10): 1059–1066.
  30. Elashoff MR, Wingrove JA, Beineke P, et al. Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics. 2011; 4: 26.
  31. Grover MP, Ballouz S, Mohanasundaram KA, et al. Novel therapeutics for coronary artery disease from genome-wide association study data. BMC Med Genomics. 2015; 8 Suppl 2: S1.
  32. Dimitrakopoulou K, Vrahatis AG, Bezerianos A. Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism. BMC Genomics. 2015; 16: 147.
  33. Sharma R, Zhou MM. Partners in crime: The role of tandem modules in gene transcription. Protein Sci. 2015; 24(9): 1347–1359.
  34. Kurozumi K, Nishita M, Yamaguchi K, et al. BRAM1, a BMP receptor-associated molecule involved in BMP signalling. Genes Cells. 1998; 3(4): 257–264.
  35. Wen H, Li Y, Xi Y, et al. ZMYND11 links histone H3.3K36me3 to transcription elongation and tumour suppression. Nature. 2014; 508(7495): 263–268.
  36. Ansieau S, Sergeant A. [BS69 and RACK7, a potential novel class of tumor suppressor genes]. Pathol Biol (Paris). 2003; 51(7): 397–399.
  37. Cotoi OS, Dunér P, Ko N, et al. Plasma S100A8/A9 correlates with blood neutrophil counts, traditional risk factors, and cardiovascular disease in middle-aged healthy individuals. Arterioscler Thromb Vasc Biol. 2014; 34(1): 202–210.
  38. Viemann D, Strey A, Janning A, et al. Myeloid-related proteins 8 and 14 induce a specific inflammatory response in human microvascular endothelial cells. Blood. 2005; 105(7): 2955–2962.
  39. Viemann D, Barczyk K, Vogl T, et al. MRP8/MRP14 impairs endothelial integrity and induces a caspase-dependent and -independent cell death program. Blood. 2007; 109(6): 2453–2460.
  40. Imbalzano E, Mandraffino G, Casciaro M, et al. Pathophysiological mechanism and therapeutic role of S100 proteins in cardiac failure: a systematic review. Heart Fail Rev. 2016; 21(5): 463–473.
  41. Nagareddy PR, Murphy AJ, Stirzaker RA, et al. Hyperglycemia promotes myelopoiesis and impairs the resolution of atherosclerosis. Cell Metab. 2013; 17(5): 695–708.
  42. Peng WH, Jian WX, Li HL, et al. Increased serum myeloid-related protein 8/14 level is associated with atherosclerosis in type 2 diabetic patients. Cardiovasc Diabetol. 2011; 10: 41.
  43. Poduri A, Bahl A, Talwar KK, et al. Proteomic analysis of circulating human monocytes in coronary artery disease. Mol Cell Biochem. 2012; 360(1-2): 181–188.
  44. Blin J, Ahmad Z, Rampal LR, et al. Preliminary assessment of differential expression of candidate genes associated with atherosclerosis. Genes Genet Syst. 2013; 88(3): 199–209.
  45. Bendeck MP, Zempo N, Clowes AW, et al. Smooth muscle cell migration and matrix metalloproteinase expression after arterial injury in the rat. Circ Res. 1994; 75(3): 539–545.
  46. Ye S. Influence of matrix metalloproteinase genotype on cardiovascular disease susceptibility and outcome. Cardiovasc Res. 2006; 69(3): 636–645.
  47. Kai H, Ikeda H, Yasukawa H, et al. Peripheral blood levels of matrix metalloproteases-2 and -9 are elevated in patients with acute coronary syndromes. J Am Coll Cardiol. 1998; 32(2): 368–372.
  48. Blankenberg S, Rupprecht HJ, Poirier O, et al. AtheroGene Investigators. Plasma concentrations and genetic variation of matrix metalloproteinase 9 and prognosis of patients with cardiovascular disease. Circulation. 2003; 107(12): 1579–1585.

Important: This website uses cookies. More >>

The cookies allow us to identify your computer and find out details about your last visit. They remembering whether you've visited the site before, so that you remain logged in - or to help us work out how many new website visitors we get each month. Most internet browsers accept cookies automatically, but you can change the settings of your browser to erase cookies or prevent automatic acceptance if you prefer.

By "Via Medica sp. z o.o." sp.k., ul. Świętokrzyska 73, 80–180 Gdańsk, Poland
tel.:+48 58 320 94 94, fax:+48 58 320 94 60, e-mail: viamedica@viamedica.pl