Network motif‑based method for identifying coronary artery disease

Li,Yin; Cong,Yan; Zhao,Yun

doi:10.3892/etm.2016.3299

Network motif‑based method for identifying coronary artery disease

Authors:
- Yin Li
- Yan Cong
- Yun Zhao
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Affiliations: Emergency Department, Huadong Hospital, Fudan University, Shanghai 200040, P.R. China
Published online on: April 27, 2016 https://doi.org/10.3892/etm.2016.3299
Pages: 257-261
Copyright: © Li 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 aimed to develop a more efficient method for identifying coronary artery disease (CAD) than the conventional method using individual differentially expressed genes (DEGs). GSE42148 gene microarray data were downloaded, preprocessed and screened for DEGs. Additionally, based on transcriptional regulation data obtained from ENCODE database and protein‑protein interaction data from the HPRD, the common genes were downloaded and compared with genes annotated from gene microarrays to screen additional common genes in order to construct an integrated regulation network. FANMOD was then used to detect significant three‑gene network motifs. Subsequently, GlobalAncova was used to screen differential three‑gene network motifs between the CAD group and the normal control data from GSE42148. Genes involved in the differential network motifs were then subjected to functional annotation and pathway enrichment analysis. Finally, clustering analysis of the CAD and control samples was performed based on individual DEGs and the top 20 network motifs identified. In total, 9,008 significant three‑node network motifs were detected from the integrated regulation network; these were categorized into 22 interaction modes, each containing a minimum of one transcription factor. Subsequently, 1,132 differential network motifs involving 697 genes were screened between the CAD and control group. The 697 genes were enriched in 154 gene ontology terms, including 119 biological processes, and 14 KEGG pathways. Identifying patients with CAD based on the top 20 network motifs provided increased accuracy compared with the conventional method based on individual DEGs. The results of the present study indicate that the network motif‑based method is more efficient and accurate for identifying CAD patients than the conventional method based on individual DEGs.

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1.	Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S and Murphy A: Growing epidemic of coronary heart disease in low- and middle-income countries. Curr Probl Cardiol. 35:72–115. 2010. View Article : Google Scholar : PubMed/NCBI
2.	Jaarsma C, Leiner T, Bekkers SC, Crijns HJ, Wildberger JE, Nagel E, Nelemans PJ and Schalla S: Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: A meta-analysis. J Am Coll Cardiol. 59:1719–1728. 2012. View Article : Google Scholar : PubMed/NCBI
3.	Beltrame JF, Dreyer R and Tavella R: Epidemiology of coronary artery disease. Coronary Artery Disease - Current Concepts in Epidemiology, Pathophysiology, Diagnostics and Treatment. Gaze D: (Rijeka). InTech. 1–30. 2012.
4.	Semmlow J and Rahalkar K: Acoustic detection of coronary artery disease. Annu Rev Biomed Eng. 9:449–469. 2007. View Article : Google Scholar : PubMed/NCBI
5.	Chen L, Qu X, Cao M, Zhou Y, Li W, Liang B, Li W, He W, Feng C, Jia X and He Y: Identification of breast cancer patients based on human signaling network motifs. Sci Rep. 3:33682013.PubMed/NCBI
6.	Doncic A and Skotheim JM: Feedforward regulation ensures stability and rapid reversibility of a cellular state. Mol Cell. 50:856–868. 2013. View Article : Google Scholar : PubMed/NCBI
7.	Albert I and Albert R: Conserved network motifs allow protein-protein interaction prediction. Bioinformatics. 20:3346–3352. 2004. View Article : Google Scholar : PubMed/NCBI
8.	Itzkovitz S, Levitt R, Kashtan N, Milo R, Itzkovitz M and Alon U: Coarse-graining and self-dissimilarity of complex networks. Phys Rev E Stat Nonlin Soft Matter Phys. 71:0161272005. View Article : Google Scholar : PubMed/NCBI
9.	Kalir S, McClure J, Pabbaraju K, Southward C, Ronen M, Leibler S, Surette MG and Alon U: Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science. 292:2080–2083. 2001. View Article : Google Scholar : PubMed/NCBI
10.	Wingrove JA, Daniels SE, Sehnert AJ, Tingley W, Elashoff MR, Rosenberg S, Buellesfeld L, Grube E, Newby LK, Ginsburg GS and Kraus WE: Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ Cardiovasc Gene. 1:31–38. 2008. View Article : Google Scholar
11.	Sinnaeve PR, Donahue MP, Grass P, Seo D, Vonderscher J, Chibout SD, Kraus WE, Sketch M Jr, Nelson C and Ginsburg GS: Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS One. 4:e70372009. View Article : Google Scholar : PubMed/NCBI
12.	Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. P Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar
13.	Ferrari F, Bortoluzzi S, Coppe A, Sirota A, Safran M, Shmoish M, Ferrari S, Lancet D, Danieli GA and Bicciato S: Novel definition files for human GeneChips based on GeneAnnot. BMC Bioinformatics. 8:4462007. View Article : Google Scholar : PubMed/NCBI
14.	Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, et al: Architecture of the human regulatory network derived from ENCODE data. Nature. 489:91–100. 2012. View Article : Google Scholar : PubMed/NCBI
15.	Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, et al: Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32:D497–D501. 2004. View Article : Google Scholar : PubMed/NCBI
16.	Wernicke S and Rasche F: FANMOD: A tool for fast network motif detection. Bioinformatics. 22:1152–1153. 2006. View Article : Google Scholar : PubMed/NCBI
17.	Hummel M, Meister R and Mansmann U: GlobalANCOVA: Exploration and assessment of gene group effects. Bioinformatics. 24:78–85. 2008. View Article : Google Scholar : PubMed/NCBI
18.	Aoki KF and Kanehisa M: Using the KEGG database resource. Curr Protoc Bioinformatics Unit. 1:1.12.1–1.12.43. 2005.
19.	Dimmer EC, Huntley RP, Alam-Faruque Y, Sawford T, O'Donovan C, Martin MJ, Bely B, Browne P, Mun Chan W and Eberhardt R: The UniProt-GO annotation database in 2011. Nucleic Acids Res. 40:D565–D570. 2012. View Article : Google Scholar : PubMed/NCBI
20.	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:32003. View Article : Google Scholar
21.	Larsson O, Wahlestedt C and Timmons JA: Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinformatics. 6:1292005. View Article : Google Scholar : PubMed/NCBI
22.	Bennett MR: Apoptosis of vascular smooth muscle cells in vascular remodelling and atherosclerotic plaque rupture. Cardiovasc Res. 41:361–368. 1999. View Article : Google Scholar : PubMed/NCBI
23.	Kockx MM: Apoptosis in the atherosclerotic plaque quantitative and qualitative aspects. Arterioscler Thromb Vasc Biol. 18:1519–1522. 1998. View Article : Google Scholar : PubMed/NCBI
24.	Bennett MR, Evan GI and Schwartz SM: Apoptosis of human vascular smooth muscle cells derived from normal vessels and coronary atherosclerotic plaques. J Clin Invest. 95:22661995. View Article : Google Scholar : PubMed/NCBI
25.	Chinnaiyan AM, Tepper CG, Seldin MF, O'Rourke K, Kischkel FC, Hellbardt S, Krammer PH, Peter ME and Dixit VM: FADD/MORT1 is a common mediator of CD95 (Fas/APO-1) and tumor necrosis factor receptor-induced apoptosis. J Biol Chem. 271:4961–4965. 1996. View Article : Google Scholar : PubMed/NCBI
26.	Chinnaiyan AM, O'Rourke K, Tewari M and Dixit VM: FADD, a novel death domain-containing protein, interacts with the death domain of Fas and initiates apoptosis. Cell. 81:505–512. 1995. View Article : Google Scholar : PubMed/NCBI
27.	Schneider P, Thome M, Burns K, Bodmer J-L, Hofmann K, Kataoka T, Holler N and Tschopp J: TRAIL receptors 1 (DR4) and 2 (DR5) signal FADD-dependent apoptosis and activate NF-kappaB. Immunity. 7:831–836. 1997. View Article : Google Scholar : PubMed/NCBI
28.	Imanishi T, McBride J, Ho Q, O'Brien KD, Schwartz SM and Han DK: Expression of cellular FLICE-inhibitory protein in human coronary arteries and in a rat vascular injury model. Am J Pathol. 156:125–137. 2000. View Article : Google Scholar : PubMed/NCBI
29.	Minami R, Shimada M, Yokosawa H and Kawahara H: Scythe regulates apoptosis through modulating ubiquitin-mediated proteolysis of the Xenopus elongation factor XEF1AO. Biochem J. 405:495–501. 2007. View Article : Google Scholar : PubMed/NCBI
30.	Witztum JL and Steinberg D: Role of oxidized low density lipoprotein in atherogenesis. J Clin Invest. 88:1785–1792. 1991. View Article : Google Scholar : PubMed/NCBI
31.	de Nigris F, Youssef T, Ciafré S, Franconi F, Anania V, Condorelli G, Palinski W and Napoli C: Evidence for oxidative activation of c-Myc-dependent nuclear signaling in human coronary smooth muscle cells and in early lesions of Watanabe heritable hyperlipidemic rabbits: Protective effects of vitamin E. Circulation. 102:2111–2117. 2000. View Article : Google Scholar : PubMed/NCBI
32.	Goff DC, Bertoni AG, Kramer H, Bonds D, Blumenthal RS, Tsai MY and Psaty BM: Dyslipidemia prevalence, treatment, and control in the Multi-Ethnic Study of Atherosclerosis (MESA): Gender, ethnicity, and coronary artery calcium. Circulation. 113:647–656. 2006. View Article : Google Scholar : PubMed/NCBI
33.	Sellak H, Choi C, Browner N and Lincoln TM: Upstream stimulatory factors (USF-1/USF-2) regulate human cGMP-dependent protein kinase I gene expression in vascular smooth muscle cells. J Biol Chem. 280:18425–18433. 2005. View Article : Google Scholar : PubMed/NCBI
34.	Naukkarinen J, Gentile M, Soro-Paavonen A, Saarela J, Koistinen HA, Pajukanta P, Taskinen M-R and Peltonen L: USF1 and dyslipidemias: Converging evidence for a functional intronic variant. Hum Mol Genet. 14:2595–2605. 2005. View Article : Google Scholar : PubMed/NCBI
35.	Laurila P-P, Naukkarinen J, Kristiansson K, Ripatti S, Kauttu T, Silander K, Salomaa V, Perola M, Karhunen PJ, Barter PJ, et al: Genetic association and interaction analysis of USF1 and APOA5 on lipid levels and atherosclerosis. Arterioscler Thromb Vasc Biol. 30:346–352. 2010. View Article : Google Scholar : PubMed/NCBI
36.	Rosenquist TH, Bennett GD, Brauer PR, Stewart ML, Chaudoin TR and Finnell RH: Microarray analysis of homocysteine-responsive genes in cardiac neural crest cells in vitro. Dev Dyn. 236:1044–1054. 2007. View Article : Google Scholar : PubMed/NCBI
37.	McCully KS: The biomedical significance of homocysteine. J Sci Explor. 15:5–20. 2001.