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The Scientific World Journal
Volume 2013, Article ID 951416, 7 pages
Research Article

Comparison of Nasal Epithelial Smoking-Induced Gene Expression on Affymetrix Exon 1.0 and Gene 1.0 ST Arrays

1Division of Computational Biomedicine, Boston University School of Medicine, 72 East Concord Street, E631, Boston, MA 02118, USA
2Division of Intramural Research, National Heart, Lung and Blood Institute, The NHLBI’s Framingham Heart Study, 73 Mt. Wayte Avenue Suite 2, Framingham, MA 01702, USA
3Pulmonary Center, Boston University Medical Center, 715 Albany Street, Boston, MA 02118, USA

Received 19 November 2012; Accepted 2 January 2013

Academic Editors: X. Li, Z. Su, and X. Xu

Copyright © 2013 Xiaoling Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We have previously defined the impact of tobacco smoking on nasal epithelium gene expression using Affymetrix Exon 1.0 ST arrays. In this paper, we compared the performance of the Affymetrix GeneChip Human Gene 1.0 ST array with the Human Exon 1.0 ST array for detecting nasal smoking-related gene expression changes. RNA collected from the nasal epithelium of five current smokers and five never smokers was hybridized to both arrays. While the intersample correlation within each array platform was relatively higher in the Gene array than that in the Exon array, the majority of the genes most changed by smoking were tightly correlated between platforms. Although neither array dataset was powered to detect differentially expressed genes (DEGs) at a false discovery rate (FDR) , we identified more DEGs than expected by chance using the Gene ST array. These findings suggest that while both platforms show a high degree of correlation for detecting smoking-induced differential gene expression changes, the Gene ST array may be a more cost-effective platform in a clinical setting for gene-level genomewide expression profiling and an effective tool for exploring the host response to cigarette smoking and other inhaled toxins.