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Original research
Bronchial gene expression signature associated with rate of subsequent FEV1 decline in individuals with and at risk of COPD


Background COPD is characterised by progressive lung function decline. Leveraging prior work demonstrating bronchial airway COPD-associated gene expression alterations, we sought to determine if there are alterations associated with differences in the rate of FEV1 decline.

Methods We examined gene expression among ever smokers with and without COPD who at baseline had bronchial brushings profiled by Affymetrix microarrays and had longitudinal lung function measurements (n=134; mean follow-up=6.38±2.48 years). Gene expression profiles associated with the rate of FEV1 decline were identified by linear modelling.

Results Expression differences in 171 genes were associated with rate of FEV1 decline (false discovery rate <0.05). The FEV1 decline signature was replicated in an independent dataset of bronchial biopsies from patients with COPD (n=46; p=0.018; mean follow-up=6.76±1.32 years). Genes elevated in individuals with more rapid FEV1 decline are significantly enriched among the genes altered by modulation of XBP1 in two independent datasets (Gene Set Enrichment Analysis (GSEA) p<0.05) and are enriched in mucin-related genes (GSEA p<0.05).

Conclusion We have identified and replicated an airway gene expression signature associated with the rate of FEV1 decline. Aspects of this signature are related to increased expression of XBP1-regulated genes, a transcription factor involved in the unfolded protein response, and genes related to mucin production. Collectively, these data suggest that molecular processes related to the rate of FEV1 decline can be detected in airway epithelium, identify a possible indicator of FEV1 decline and make it possible to detect, in an early phase, ever smokers with and without COPD most at risk of rapid FEV1 decline.

  • respiratory measurement
  • airway epithelium

Data availability statement

Data are available in a public, open access repository. The data are available on GEO as GSE37147.

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