Abstract
Introduction: COPD is the third leading cause of death worldwide, however, our knowledge of the biological mechanisms underlying the development and progression of this disease is still limited.
Aims: We aimed to identify genes differentially expressed in airway-derived cells between COPD cases and controls, since these may highlight pathways relevant to COPD.
Methods: We analysed RNA sequencing data in COPD cases and controls from the EvA study, as part of the AirPROM project. Epithelial brushing (305 COPD cases and 232 controls) and bronchoalveolar lavage (BAL) (182 COPD cases and 174 controls) were analysed. Lasso regression was used to assess whether differentially expressed genes may be able to predict case control status. Genes were also clustered into modules according to their co-expression across individuals, and the association of these modules with COPD status and other related traits were tested using weighted gene co-expression network analysis (WGCNA).
Results: The lasso regression analysis was applied to training and testing sets of randomly selected individuals. Average predictive values across 50 iterations of ∼80% for brushing and ∼70% for lavage were obtained. Amongst the 7 genes that were selected more than 30 times WNK4, lysine-deficient protein kinase 4, was selected the most (48 times) in the epithelial brushings and was also highlighted by the WGCNA analysis, suggesting its potential relevance in processes underlying COPD.
Conclusions: Genes differentially expressed between COPD cases and controls were identified. These findings may lead to new biological pathways relevant to COPD.
This research, part of AirPROM, received funding from the European Union (grant n°270194).
- Copyright ©the authors 2016