{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T18:11:11Z","timestamp":1778004671092,"version":"3.51.4"},"reference-count":35,"publisher":"European Respiratory Society (ERS)","issue":"2","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["ERJ Open Res"],"accepted":{"date-parts":[[2025,9,6]]},"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>Rationale<\/jats:title>\n                    <jats:p>COPD is characterised by chronic airflow limitation and persistent inflammation. Inhaled corticosteroids (ICS) are often used to reduce airway inflammation in patients. However, the response to ICS treatment varies among patients, and blood eosinophils may not fully reflect treatment effectiveness. In this study, we aim to identify gene modules associated with ICS responsiveness and assess the underlying biological pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We included 55 patients from the GLUCOLD study with mild\u2013moderate COPD treated with ICS for 6\u2005months with available gene expression data from biopsies. Treatment response was defined as changes in post-bronchodilator forced expiratory volume in 1\u2005s (FEV\n                      <jats:sub>1<\/jats:sub>\n                      ) % predicted, post-bronchodilator FEV\n                      <jats:sub>1<\/jats:sub>\n                      \/forced vital capacity and residual volume\/total lung capacity. To identify gene modules significantly correlated with ICS treatment responsiveness, we applied Weighted Gene Co-Expression Network Analysis (WGCNA). To explore the biological relevance of these modules, functional enrichment analysis was conducted using the STRING database, and key genes were identified through Gene Network Inference using the Ensemble of Trees (GENIE3) approach.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified four gene modules associated to ICS-induced improvement in FEV\n                      <jats:sub>1<\/jats:sub>\n                      % predicted. Pathway enrichment analysis revealed key biological pathways including cilium function, positive regulation of various metabolic processes and inflammation. These pathways were also reflected by key genes including\n                      <jats:italic>LRRC6<\/jats:italic>\n                      ,\n                      <jats:italic>IFT46<\/jats:italic>\n                      ,\n                      <jats:italic>AKT2<\/jats:italic>\n                      and\n                      <jats:italic>PIK3C3<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study identified gene modules and pathways associated with ICS responsiveness in COPD, providing a potential mechanistic explanation for the variability in ICS treatment responsiveness in COPD.<\/jats:p>\n                  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