Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease

Zhang,Huan; Li,Tao; Wu,Guanji; Ma,Feng

doi:10.3892/etm.2014.1610

Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease

Authors:
- Huan Zhang
- Tao Li
- Guanji Wu
- Feng Ma
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Affiliations: Department of Cardiovascular Medicine, Xi'an Central Hospital, Xi'an, Shaanxi 710003, P.R. China
Published online on: March 7, 2014 https://doi.org/10.3892/etm.2014.1610
Pages: 1151-1154

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Abstract

Coronary artery disease (CAD) is the most common type of cardiovascular disease and leading cause of mortality worldwide. Microarray technology for gene expression analysis has facilitated the identification of the molecular mechanism that underlies the pathogenesis of CAD. Previous studies have primarily used variance or regression analysis, without considering array specific factors. Thus, the aim of the present study was to investigate the mechanism of CAD using partial least squares (PLS)‑based analysis, which was integrated with the Monte Carlo technique. Microarray analysis was performed with a data set of 110 CAD patients and 111 controls obtained from the Gene Expression Omnibus database. A total of 390 dysregulated genes were acquired. Significantly increased representations of dysregulated genes in Gene Ontology items, including transforming growth factor β‑activated receptor activity and acyl‑CoA oxidase activity, were identified. Network analysis revealed three hub genes with a degree of >10, including ESR1, ITGA4 and ARRB2. The results of the present study provide novel information on the gene expression signatures of CAD patients and offer further theoretical support for future therapeutic study.

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1	Thomas AC, Knapman PA, Krikler DM and Davies MJ: Community study of the causes of ‘natural’ sudden death. BMJ. 297:1453–1456. 1988.
2	Hiltunen MO, Tuomisto TT, Niemi M, et al: Changes in gene expression in atherosclerotic plaques analyzed using DNA array. Atherosclerosis. 165:23–32. 2002. View Article : Google Scholar : PubMed/NCBI
3	Nanni L, Romualdi C, Maseri A and Lanfranchi G: Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. J Mol Cell Cardiol. 41:934–948. 2006. View Article : Google Scholar : PubMed/NCBI
4	Randi AM, Biguzzi E, Falciani F, et al: Identification of differentially expressed genes in coronary atherosclerotic plaques from patients with stable or unstable angina by cDNA array analysis. J Thromb Haemost. 1:829–835. 2003. View Article : Google Scholar
5	Seo D, Wang T, Dressman H, et al: Gene expression phenotypes of atherosclerosis. Arterioscler Thromb Vasc Biol. 24:1922–1927. 2004. View Article : Google Scholar
6	Cagnin S, Biscuola M, Patuzzo C, et al: Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries. BMC Genomics. 10:132009. View Article : Google Scholar : PubMed/NCBI
7	Sluimer JC, Kisters N, Cleutjens KB, et al: Dead or alive: gene expression profiles of advanced atherosclerotic plaques from autopsy and surgery. Physiol Genomics. 30:335–341. 2007. View Article : Google Scholar : PubMed/NCBI
8	Chakraborty S and Datta S and Datta S: Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Bioinformatics. 28:799–806. 2012. View Article : Google Scholar : PubMed/NCBI
9	Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste BM and Sterna C: Elimination of uninformative variables for multivariate calibration. Anal Chem. 68:3851–3858. 1996. View Article : Google Scholar : PubMed/NCBI
10	Picard RR and Cook RD: Cross-validation of regression models. J Am Stat Assoc. 79:575–583. 1984. View Article : Google Scholar
11	Xu QS, Liang YZ and Du YP: Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration. J Chemom. 18:112–120. 2004. View Article : Google Scholar
12	Gourvénec S, Fernández Pierna JA, Massart DL and Rutledge DN: An evaluation of the PoLiSh smoothed regression and the Monte Carlo cross-validation for the determination of the complexity of a PLS model. Chemometr Intell Lab Syst. 68:41–51. 2003.
13	Cai WS, Li YK and Shao XG: A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometr Intell Lab. 90:188–194. 2008. View Article : Google Scholar
14	Felker GM, Shaw LK and O’Connor CM: A standardized definition of ischemic cardiomyopathy for use in clinical research. J Am Coll Cardiol. 39:210–218. 2002. View Article : Google Scholar : PubMed/NCBI
15	Mark DB, Nelson CL, Califf RM, et al: Continuing evolution of therapy for coronary artery disease. Initial results from the era of coronary angioplasty. Circulation. 89:2015–2025. 1994. View Article : Google Scholar : PubMed/NCBI
16	Irizarry RA, Hobbs B, Collin F, et al: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar
17	Helland IS: On the structure of partial least squares regression. Commun Stat-Simulation Comput. 17:581–607. 1988. View Article : Google Scholar
18	Helland IS: Partial least squares regression and statistical model. Scand J Stat. 17:97–144. 1990.
19	Martins JPA, Teófilo RF and Ferreira MMC: Computational performance and cross-validation error precision of five PLS algorithms using designed and real data sets. J Chemom. 24:320–332. 2010.
20	Gosselin R, Rodrigue D and Duchesne C: A bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chemometr Intell Lab Syst. 100:12–21. 2010. View Article : Google Scholar
21	Ashburner M, Ball CA, Blake JA, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
22	Stelzl U, Worm U, Lalowski M, et al: A human protein-protein interaction network: a resource for annotating the proteome. Cell. 122:957–968. 2005.PubMed/NCBI
23	Shannon P, Markiel A, Ozier O, et al: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
24	Ellmers LJ, Scott NJ, Medicherla S, et al: Transforming growth factor-beta blockade down-regulates the renin-angiotensin system and modifies cardiac remodeling after myocardial infarction. Endocrinology. 149:5828–5834. 2008. View Article : Google Scholar : PubMed/NCBI
25	Liu IM, Tzeng TF, Liou SS and Chang CJ: Regulation of obesity and lipid disorders by extracts from Angelica acutiloba root in high-fat diet-induced obese rats. Phytother Res. 26:223–230. 2012.PubMed/NCBI
26	Almeida S and Hutz MH: Estrogen receptor 1 gene polymorphisms and coronary artery disease in the Brazilian population. Braz J Med Biol Res. 39:447–454. 2006. View Article : Google Scholar : PubMed/NCBI
27	Lawlor DA, Timpson N, Ebrahim S, Day IN and Smith GD: The association of oestrogen receptor alpha-haplotypes with cardiovascular risk factors in the British Women’s Heart and Health Study. Eur Heart J. 27:1597–1604. 2006.PubMed/NCBI
28	Wei CD, Zheng HY, Wu W, et al: Meta-analysis of the association of the rs2234693 and rs9340799 polymorphisms of estrogen receptor alpha gene with coronary heart disease risk in Chinese Han population. Int J Med Sci. 10:457–466. 2013. View Article : Google Scholar : PubMed/NCBI
29	Dettman RW, Pae SH, Morabito C and Bristow J: Inhibition of alpha4-integrin stimulates epicardial-mesenchymal transformation and alters migration and cell fate of epicardially derived mesenchyme. Dev Biol. 257:315–328. 2003. View Article : Google Scholar