Identification of microRNA-mRNA interactions in atrial fibrillation using microarray expression profiles and bioinformatics analysis

Wang,Tao; Wang,Bin

doi:10.3892/mmr.2016.5106

Identification of microRNA-mRNA interactions in atrial fibrillation using microarray expression profiles and bioinformatics analysis

Authors:
- Tao Wang
- Bin Wang
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Affiliations: Department of Cardiology, Shandong Jiaotong Hospital, Jinan, Shandong 250031, P.R. China, Department of Otorhinolaryngology, Hangzhou First People's Hospital, Hangzhou, Zhejiang 310006, P.R. China
Published online on: April 12, 2016 https://doi.org/10.3892/mmr.2016.5106
Pages: 4535-4540
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study integrated microRNA (miRNA) and mRNA expression data obtained from atrial fibrillation (AF) tissues and healthy tissues, in order to identify miRNAs and target genes that may be important in the development of AF. The GSE28954 miRNA expression profile and GSE2240 mRNA gene expression profile were downloaded from the Gene Expression Omnibus. Differentially expressed miRNAs and genes (DEGs) in AF tissues, compared with in control samples, were identified and hierarchically clustered. Subsequently, differentially expressed miRNAs and DEGs were searched for in the miRecords database and TarBase, and were used to construct a regulatory network using Cytoscape. Finally, functional analysis of the miRNA‑targeted genes was conducted. After data processing, 71 differentially expressed miRNAs and 390 DEGs were identified between AF and normal tissues. A total of 3,506 miRNA‑mRNA pairs were selected, of which 372 were simultaneously predicted by both miRecords and TarBase, and were therefore used to construct the miRNA‑mRNA regulatory network. Furthermore, 10 miRNAs and 12 targeted mRNAs were detected, which formed 14 interactive pairs. The miRNA‑targeted genes were significantly enriched into 14 Gene Ontology (GO) categories, of which the most significant was gene expression regulation (GO 10468), which was associated with 7 miRNAs and 8 target genes. These results suggest that the screened miRNAs and target genes may be target molecules in AF development, and may be beneficial for the early diagnosis and future treatment of AF.

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1	Wyse DG, Waldo AL, DiMarco JP, Domanski MJ, Rosenberg Y, Schron EB, Kellen JC, Greene HL, Mickel MC, Dalquist JE and Corley SD: Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) Investigators: A comparison of rate control and rhythm control in patients with atrial fibrillation. N Engl J Med. 347:1825–1833. 2002. View Article : Google Scholar : PubMed/NCBI
2	Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Guyton RA, Hochman JS, Kovacs RJ, et al: Management of patients with atrial fibrillation (compilation of 2006 ACCF/AHA/ESC and 2011 ACCF/AHA/HRS recommendations): A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 61:1935–1944. 2013. View Article : Google Scholar : PubMed/NCBI
3	Gilligan DM, Joyner CA and Bundy GM: Multidisciplinary collaboration for the treatment of atrial fibrillation: Convergent procedure outcomes from a single center. Innovation in CRM. 4:1396–1403. 2013.
4	Bartel DP: MicroRNAs: Target recognition and regulatory functions. Cell. 136:215–233. 2009. View Article : Google Scholar : PubMed/NCBI
5	Lu Y, Zhang Y, Wang N, Pan Z, Gao X, Zhang F, Zhang Y, Shan H, Luo X, Bai Y, et al: MicroRNA-328 contributes to adverse electrical remodeling in atrial fibrillation. Circulation. 122:2378–2387. 2010. View Article : Google Scholar : PubMed/NCBI
6	Luo X, Pan Z, Shan H, Xiao J, Sun X, Wang N, Lin H, Xiao L, Maguy A, Qi XY, et al: MicroRNA-26 governs profibrillatory inward-rectifier potassium current changes in atrial fibrillation. J Clin Invest. 123:1939–1951. 2013. View Article : Google Scholar : PubMed/NCBI
7	Chen CL, Lin JL, Lai LP, Pan CH, Huang SK and Lin CS: Altered expression of FHL1, CARP, TSC-22 and P311 provide insights into complex transcriptional regulation in pacing-induced atrial fibrillation. Biochim Biophys Acta. 1772:317–29. 2007. View Article : Google Scholar
8	Girmatsion Z, Biliczki P, Bonauer A, Wimmer-Greinecker G, Scherer M, Moritz A, Bukowska A, Goette A, Nattel S, Hohnloser SH and Ehrlich JR: Changes in microRNA-1 expression and IK1 upregulation in human atrial fibrillation. Heart Rhythm. 6:1802–1809. 2009. View Article : Google Scholar : PubMed/NCBI
9	Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, Steinmeyer K, Bleich M, Kääb S, Hinterseer M, et al: Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: Expression of a ventricular-like genomic signature. Circ Res. 96:1022–1029. 2005. View Article : Google Scholar : PubMed/NCBI
10	Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, Steinmeyer K, Bleich M, Kääb S, Pfeufer A, et al: Functional profiling of human atrial and ventricular gene expression. Pflugers Arch. 450:201–208. 2005. View Article : Google Scholar : PubMed/NCBI
11	Cooley N, Cowley MJ, Lin RC, Marasco S, Wong C, Kaye DM, Dart AM and Woodcock EA: Influence of atrial fibrillation on microRNA expression profiles in left and right atria from patients with valvular heart disease. Physiol Genomics. 44:211–219. 2012. View Article : Google Scholar
12	Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI
13	Zhang S: A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance. BMC Bioinformatics. 8:2302007. View Article : Google Scholar : PubMed/NCBI
14	Sawilowsky SS (2005): Misconceptions leading to choosing the t test over the Wilcoxon Mann-Whitney U test for shift in location parameter. Journal of Modern Applied Statistical Methods. 4(2): 598–600. 2005.
15	Shaffer JP: Multiple hypothesis testing. Annual Review of Psychology. 46:561–584. 1995. View Article : Google Scholar
16	Yeung KY and Ruzzo WL: Principal component analysis for clustering gene expression data. Bioinformatics. 17:763–774. 2001. View Article : Google Scholar : PubMed/NCBI
17	Eisen MB, Spellman PT, Brown PO and Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95:14863–14868. 1998. View Article : Google Scholar : PubMed/NCBI
18	Xiao F, Zuo Z, Cai G, Kang S, Gao X and Li T: MiRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res. 37(Database issue): D105–D110. 2009. View Article : Google Scholar :
19	Sethupathy P, Corda B and Hatzigeorgiou AG: TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA. 12:192–197. 2006. View Article : Google Scholar :
20	Peng X, Li Y, Walters KA, Rosenzweig ER, Lederer SL, Aicher LD, Proll S and Katze MG: Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers. BMC Genomics. 10:3732009. View Article : Google Scholar : PubMed/NCBI
21	Liu H, Brannon AR, Reddy AR, Alexe G, Seiler MW, Arreola A, Oza JH, Yao M, Juan D, Liou LS, et al: Identifying mRNA targets of microRNA dysregulated in cancer: With application to clear cell renal cell carcinoma. BMC Syst Biol. 4:512010. View Article : Google Scholar : PubMed/NCBI
22	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
23	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
24	Ji R, Cheng Y, Yue J, Yang J, Liu X, Chen H, Dean DB and Zhang C: MicroRNA expression signature and antisense-mediated depletion reveal an essential role of MicroRNA in vascular neointimal lesion formation. Circ Res. 100:1579–1588. 2007. View Article : Google Scholar : PubMed/NCBI
25	Luo X, Pan Z, Shan H, Xiao J, Sun X, Wang N, Lin H, Xiao L, Maguy A, Qi XY, et al: MicroRNA-26 governs profibrillatory inward-rectifier potassium current changes in atrial fibrillation. J Clin Invest. 123:1939–1951. 2013. View Article : Google Scholar : PubMed/NCBI
26	Rao PK, Kumar RM, Farkhondeh M, Baskerville S and Lodish HF: Myogenic factors that regulate expression of muscle-specific microRNAs. Proc Natl Acad Sci USA. 103:8721–8726. 2006. View Article : Google Scholar : PubMed/NCBI
27	Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, Steinmeyer K, Bleich M, Kääb S, Hinterseer M, et al: Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: Expression of a ventricular-like genomic signature. Circ Res. 96:1022–1029. 2005. View Article : Google Scholar : PubMed/NCBI
28	Mao S and Huang S: Toll-like receptors signaling in glomerular diseases. J Recept Signal Transduct Res. 34:81–84. 2014. View Article : Google Scholar
29	Kharlap MS, Timofeeva AV, Goryunova LE, Khaspekov GL, Dzemeshkevich SL, Ruskin VV, Akchurin RS, Golitsyn SP and Beabealashvilli RSh: Atrial appendage transcriptional profile in patients with atrial fibrillation with structural heart diseases. Ann N Y Acad Sci. 1091:205–217. 2006. View Article : Google Scholar
30	Wajapeyee N, Serra RW, Zhu X, Mahalingam M and Green MR: Role for IGFBP7 in senescence induction by BRAF. Cell. 141:746–747. 2010. View Article : Google Scholar : PubMed/NCBI
31	Benson MD, Li QJ, Kieckhafer K, Dudek D, Whorton MR, Sunahara RK, Iñiguez-Lluhí JA and Martens JR: SUMO modification regulates inactivation of the voltage-gated potassium channel Kv1.5. Proc Natl Acad Sci USA. 104:1805–1810. 2007. View Article : Google Scholar : PubMed/NCBI
32	Rossi S, Tsirigos A, Amoroso A, Mascellani N, Rigoutsos I, Calin GA and Volinia S: OMiR: Identification of associations between OMIM diseases and microRNAs. Genomics. 97:71–76. 2011. View Article : Google Scholar