Gene microarray analyses for potential biomarkers of single and recurrent venous thromboembolism

Zhou,Wugang; Zhang,Ke; Chen,Dongrui; Gao,Pingjin; Wang,Qiao

doi:10.3892/mmr.2015.4349

Gene microarray analyses for potential biomarkers of single and recurrent venous thromboembolism

Authors:
- Wugang Zhou
- Ke Zhang
- Dongrui Chen
- Pingjin Gao
- Qiao Wang
View Affiliations

Affiliations: Emergency Department, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, P.R. China, Department of Hypertension, Ruijin Hospital, Shanghai Key Lab of Hypertension, Shanghai Institute of Hypertension, Shanghai Jiaotong University School of Medicine, Shanghai 200025, P.R. China
Published online on: September 22, 2015 https://doi.org/10.3892/mmr.2015.4349
Pages: 7358-7366
Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Venous thromboembolism is a major cause of morbidity and mortality with a high recurrence rate. The present study aimed to explore the molecular mechanisms and potential biomarkers of single venous thromboembolism (SVTE) and recurrent venous thromboembolism (RVTE). The microarray dataset GSE19151 was downloaded from Gene Expression Omnibus, which contained data from whole blood samples from 63 healthy controls, 32 SVTE and 38 RVTE patients. Differentially expressed genes (DEGs) in the SVTE and RVTE groups compared with those in the controls were identified using the t‑test, followed by clustering analysis of DEGs and samples. Functional and pathway enrichment analyses were performed for DEGs in patients with RVTE and SVTE, as well as specific DEGs in patients with RVTE. The identified 42 DEGs in RVTE were mainly enriched in biological processes of cellular protein metabolism, gene expression and translational elongation as well as in pathways associated with ribosomes, Parkinson's disease and oxidative phosphorylation. In SVTE, 20 DEGs were identified, which were mainly involved in biological processes of biopolymer biosynthesis, translational elongation and cellular protein metabolism as well as pathways associated with ribosomes and cardiac muscle contraction. In RVTE, 22 specific DEGs were mainly involved in translational elongation, negative regulation of the force of heart contraction by chemical signals, cell proliferation, ribosomal pathways and protein export. The identified DEGs of SVTE, including COX7C and UQCRQ, may be potential biomarkers for SVTE, and the specific DEGs of RVTE, including ADRBK1, NDUFA5 and ATP5O, may be potential biomarkers for RVTE.

View Figures

View References

1	Pabinger I and Ay C: Biomarkers and venous thromboembolism. Arterioscler Thromb Vasc Biol. 29:332–336. 2009. View Article : Google Scholar : PubMed/NCBI
2	Baglin T: What happens after venous thromboembolism? J Thromb Haemost. 7(Suppl 1): 287–290. 2009. View Article : Google Scholar : PubMed/NCBI
3	Stein PD, Beemath A and Olson RE: Trends in the incidence of pulmonary embolism and deep venous thrombosis in hospitalized patients. Am J Cardiol. 95:1525–1526. 2005. View Article : Google Scholar : PubMed/NCBI
4	Nielsen JD: The incidence of pulmonary embolism during deep vein thrombosis. Phlebology. 28(Suppl 1): S29–S33. 2013. View Article : Google Scholar
5	Heit JA, Mohr DN, Silverstein MD, Petterson TM, O'Fallon WM and Melton LJ III: Predictors of recurrence after deep vein thrombosis and pulmonary embolism: A population-based cohort study. Arch Intern Med. 160:761–768. 2000. View Article : Google Scholar : PubMed/NCBI
6	Poli D and Palareti G: Assessing recurrence risk following acute venous thromboembolism: Use of algorithms. Curr Opin Pulm Med. 19:407–412. 2013. View Article : Google Scholar : PubMed/NCBI
7	Barnes DM, Wakefield TW and Rectenwald JE: Novel biomarkers associated with deep venous thrombosis: A comprehensive review. Biomarker Insights. 3:93–100. 2008.PubMed/NCBI
8	Kyrle PA, Minar E, Hirschl M, Bialonczyk C, Stain M, Schneider B, Weltermann A, Speiser W, Lechner K and Eichinger S: High plasma levels of factor VIII and the risk of recurrent venous thromboembolism. N Engl J Med. 343:457–462. 2000. View Article : Google Scholar : PubMed/NCBI
9	Comp PC and Esmon CT: Recurrent venous thromboembolism in patients with a partial deficiency of protein S. N Engl J Med. 311:1525–1528. 1984. View Article : Google Scholar : PubMed/NCBI
10	Palareti G, Cosmi B, Legnani C, Tosetto A, Brusi C, Iorio A, Pengo V, Ghirarduzzi A, Pattacini C, Testa S, et al: D-dimer testing to determine the duration of anticoagulation therapy. N Engl J Med. 355:1780–1789. 2006. View Article : Google Scholar : PubMed/NCBI
11	Pabinger I, Thaler J and Ay C: Biomarkers for prediction of venous thromboembolism in cancer. Blood. 122:2011–2018. 2013. View Article : Google Scholar : PubMed/NCBI
12	Ay C, Dunkler D, Simanek R, Thaler J, Koder S, Marosi C, Zielinski C and Pabinger I: Prediction of venous thromboembolism in patients with cancer by measuring thrombin generation: Results from the Vienna cancer and thrombosis study. J Clin Oncol. 29:2099–2103. 2011. View Article : Google Scholar : PubMed/NCBI
13	Bouman AC, Smits JJ, Ten Cate H and Ten Cate-Hoek AJ: Markers of coagulation, fibrinolysis and inflammation in relation to post-thrombotic syndrome. J Thromb Haemost. 10:1532–1538. 2012. View Article : Google Scholar : PubMed/NCBI
14	Lewis DA, Stashenko GJ, Akay OM, Price LI, Owzar K, Ginsburg GS, Chi JT and Ortel TL: Whole blood gene expression analyses in patients with single vs. recurrent venous thromboembolism. Thromb Res. 128:536–540. 2011. View Article : Google Scholar : PubMed/NCBI
15	Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R802004. View Article : Google Scholar : PubMed/NCBI
16	Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed TP: Summaries of affymetrix genechip probe level data. Nucleic Acids Res. 31:e152003. View Article : Google Scholar : PubMed/NCBI
17	Wilson CL and Miller CJ: Simpleaffy: A Bioconductor package for affymetrix quality control and data analysis. Bioinformatics. 21:3683–3685. 2005. View Article : Google Scholar : PubMed/NCBI
18	R Development Core Team: R: A Language and Environment for Statistical Computing. The R Foundation for Statistical Computing; Vienna, Austria: 2011, ISBN: 3-900051-07-0
19	Zheng G, Wang H, Wei C and Li Y: iGepros: An integrated gene and protein annotation server for biological nature exploration. BMC Bioinformatics. 12(Suppl 14): S62011. View Article : Google Scholar
20	Luo W and Brouwer C: Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 29:1830–1831. 2013. View Article : Google Scholar : PubMed/NCBI
21	Verhamme P and Bounameaux H: Direct oral anticoagulants for acute venous thromboembolism: Closing the circle? Circulation. 129:725–727. 2014. View Article : Google Scholar
22	Lippi G, Cervellin G, Franchini M and Favaloro EJ: Biochemical markers for the diagnosis of venous thromboembolism: The past, present and future. J Thromb Thrombolysis. 30:459–471. 2010. View Article : Google Scholar : PubMed/NCBI
23	Lindström MS: Emerging functions of ribosomal proteins in gene-specific transcription and translation. Biochem Biophys Res Commun. 379:167–170. 2009. View Article : Google Scholar
24	Komrokji RS, Padron E, Ebert BL and List AF: Deletion 5q MDS: Molecular and therapeutic implications. Best Pract Res Clin Haematol. 26:365–375. 2013. View Article : Google Scholar
25	Beech RD, Lowthert L, Leffert JJ, Mason PN, Taylor MM, Umlauf S, Lin A, Lee JY, Maloney K, Muralidharan A, et al: Increased peripheral blood expression of electron transport chain genes in bipolar depression. Bipolar disorders. 12:813–824. 2010. View Article : Google Scholar : PubMed/NCBI
26	Taurino C, Miller WH, McBride MW, McClure JD, Khanin R, Moreno MU, Dymott JA, Delles C and Dominiczak AF: Gene expression profiling in whole blood of patients with coronary artery disease. Clin Sci (Lond). 119:335–343. 2010. View Article : Google Scholar
27	White RH: The epidemiology of venous thromboembolism. Circulation. 107:I4–I8. 2003. View Article : Google Scholar : PubMed/NCBI
28	Textoris J, Ivorra D, Ben Amara A, Sabatier F, Ménard JP, Heckenroth H, Bretelle F and Mege JL: Evaluation of current and new biomarkers in severe preeclampsia: A microarray approach reveals thevs.ig4 gene as a potential blood biomarker. PLoS One. 8:e826382013. View Article : Google Scholar
29	White BC, Sullivan JM, DeGracia DJ, O'Neil BJ, Neumar RW, Grossman LI, Rafols JA and Krause GS: Brain ischemia and reperfusion: Molecular mechanisms of neuronal injury. J Neurol Sci. 179:1–33. 2000. View Article : Google Scholar : PubMed/NCBI
30	Choesmel V, Fribourg S, Aguissa-Touré AH, Pinaud N, Legrand P, Gazda HT and Gleizes PE: Mutation of ribosomal protein RPS24 in Diamond-Blackfan anemia results in a ribosome biogenesis disorder. Hum Mol Genet. 17:1253–1263. 2008. View Article : Google Scholar : PubMed/NCBI
31	Zee RY, Cook NR, Cheng S, Erlich HA, Lindpaintner K and Ridker PM: Polymorphism in the beta2-adrenergic receptor and lipoprotein lipase genes as risk determinants for idiopathic venous thromboembolism: A multilocus, population-based, prospective genetic analysis. Circulation. 113:2193–2200. 2006. View Article : Google Scholar : PubMed/NCBI
32	Tachibana H, Naga Prasad SV, Lefkowitz RJ, Koch WJ and Rockman HA: Level of beta-adrenergic receptor kinase 1 inhibition determines degree of cardiac dysfunction after chronic pressure overload-induced heart failure. Circulation. 111:591–597. 2005. View Article : Google Scholar : PubMed/NCBI
33	Ito K, Akazawa H, Tamagawa M, Furukawa K, Ogawa W, Yasuda N, Kudo Y, Liao CH, Yamamoto R, Sato T, et al: PDK1 coordinates survival pathways and beta-adrenergic response in the heart. Proc Natl Acad Sci USA. 106:8689–8694. 2009. View Article : Google Scholar : PubMed/NCBI
34	Lombardi MS, Vroon A, Sodaar P, van Muiswinkel FL, Heijnen CJ and Kavelaars A: Down-regulation of GRK2 after oxygen and glucose deprivation in rat hippocampal slices: Role of the PI3-kinase pathway. J Neurochem. 102:731–740. 2007. View Article : Google Scholar : PubMed/NCBI
35	Hideshima T, Nakamura N, Chauhan D and Anderson KC: Biologic sequelae of interleukin-6 induced PI3-K/Akt signaling in multiple myeloma. Oncogene. 20:5991–6000. 2001. View Article : Google Scholar : PubMed/NCBI
36	Chou JL, Shenoy DV, Thomas N, Choudhary PK, Laferla FM, Goodman SR and Breen GA: Early dysregulation of the mitochondrial proteome in a mouse model of Alzheimer's disease. J Proteomics. 74:466–479. 2011. View Article : Google Scholar : PubMed/NCBI
37	Weydt P, Pineda VV, Torrence AE, Libby RT, Satterfield TF, Lazarowski ER, Gilbert ML, Morton GJ, Bammler TK, Strand AD, et al: Thermoregulatory and metabolic defects in Huntington's disease transgenic mice implicate PGC-1alpha in Huntington's disease neurodegeneration. Cell Metab. 4:349–362. 2006. View Article : Google Scholar : PubMed/NCBI
38	Davi G, Di Minno G, Coppola A, Andria G, Cerbone AM, Madonna P, Tufano A, Falco A, Marchesani P, Ciabattoni G and Patrono C: Oxidative stress and platelet activation in homozygous homocystinuria. Circulation. 104:1124–1128. 2001. View Article : Google Scholar : PubMed/NCBI
39	El Kossi MM and Zakhary MM: Oxidative stress in the context of acute cerebrovascular stroke. Stroke. 31:1889–1892. 2000. View Article : Google Scholar : PubMed/NCBI