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Published ahead of print on October 10, 2008, doi:10.1165/rcmb.2008-0114OC
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American Journal of Respiratory Cell and Molecular Biology. Vol. 40, pp. 359-367, 2009
© 2009 American Thoracic Society
DOI: 10.1165/rcmb.2008-0114OC

Molecular Biomarkers for Quantitative and Discrete COPD Phenotypes

Soumyaroop Bhattacharya1,*,{ddagger}, Sorachai Srisuma1,4,*, Dawn L. DeMeo2, Steven D. Shapiro1,§, Raphael Bueno3, Edwin K. Silverman2, John J. Reilly1,§ and Thomas J. Mariani1,{ddagger}

1 Department of Pulmonary Medicine, 2 The Channing Laboratory, and 3 Thoracic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; and 4 Department of Physiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Correspondence and requests for reprints should be addressed to Thomas J. Mariani, Ph.D., Division of Neonatology and Center for Pediatric Biomedical Research, Department of Pediatrics, University of Rochester, Box 703, 601 Elmwood Avenue, Rochester, NY 14642. E-mail: Tom_Mariani{at}URMC.Rochester.edu

Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disorder with complex pathological features and largely unknown etiology. The identification of biomarkers for this disease could aid the development of methods to facilitate earlier diagnosis, the classification of disease subtypes, and provide a means to define therapeutic response. To identify gene expression biomarkers, we completed expression profiling of RNA derived from the lung tissue of 56 subjects with varying degrees of airflow obstruction using the Affymetrix U133 Plus 2.0 array. We applied multiple, independent analytical methods to define biomarkers for either discrete or quantitative disease phenotypes. Analysis of differential expression between cases (n = 15) and controls (n = 18) identified a set of 65 discrete biomarkers. Correlation of gene expression with quantitative measures of airflow obstruction (FEV1%predicted or FEV1/FVC) identified a set of 220 biomarkers. Biomarker genes were enriched in functions related to DNA binding and regulation of transcription. We used this group of biomarkers to predict disease in an unrelated data set, generated from patients with severe emphysema, with 97% accuracy. Our data contribute to the understanding of gene expression changes occurring in the lung tissue of patients with obstructive lung disease and provide additional insight into potential mechanisms involved in the disease process. Furthermore, we present the first gene expression biomarker for COPD validated in an independent data set.

Key Words: microarray • gene expression • emphysema • lung function


CLINICAL RELEVANCE

Chronic obstructive pulmonary disease includes a broad spectrum of histopathologic findings and respiratory symptoms, with a complex disease pathogenesis. We used genome-wide expression profiling to identify biomarkers for disease-related phenotypes.

 



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