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Molecular Markers for Quantitative and Discrete COPD Phenotypes
Author(s) -
Mariani Thomas J,
Bhattacharya Soumyaroop,
Srisuma Sorachai,
DeMeo Dawn L.,
Shapiro Steven D.,
Bueno Raphael,
Silverman Edwin K.,
Reilly John J.
Publication year - 2007
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.21.5.a8-c
Subject(s) - copd , gene expression profiling , medicine , gene expression , pathology , pulmonary disease , phenotype , gene , biology , genetics
To identify gene expression markers for chronic obstructive pulmonary disease (COPD), we performed genome‐wide expression profiling of lung tissue from 56 patients with a solitary pulmonary nodule undergoing surgical resection. Analysis of differential expression between cases (FEV1<70% predicted, FEV1/FVC<0.7) and controls (FEV1>80% predicted, FEV1/FVC>0.7) identified a set of 65 transcripts representing discrete markers associated with COPD. Correlation of gene expression with a quantitative measure of airflow obstruction (FEV1), using both Pearson and Spearman coefficients (p<0.01), identified a set of 65 marker transcripts. A total of 43 transcripts were identified that showed evidence of significant correlation (p<0.05) with quantitative traits and differential expression between cases and controls. Finally, we used this group of disease markers to develop a gene expression signature for COPD capable of predicting disease in a previously published data set generated from patients with severe emphysema undergoing lung volume reduction surgery (Spira et. al., AJRCMB. 2004). Although these data sets represent distinct populations of COPD patients, we identified a molecular signature comprising 15 probe sets representing 8 genes (CIRBP, ZNF207, ARHGEF12, CTSK, ZF, CROP, ARHGAP29, HPGD) capable of 94% predictive 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. Funded by NHLBI