Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-basedanalysis implemented by SWIM software can be exploited to identify key molecular switches- called "switch genes" - for the disease. Genes contributing to common biological processes or defininggiven cell types are usually co-regulated and co-expressed, forming expression network modules.Consistently, we found that the COPD correlation network built by SWIM consists of three wellcharacterizedmodules: one populated by switch genes, all up-regulated in COPD cases and relatedto the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK,LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulatedin COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative modulegenes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switchgenes including PRDX4, whose activation strongly correlates with the activation of known COPD GWASinteractors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identifykey network modules related to complex diseases like COPD.