Integrated transcriptomic correlation network analysis identifies COPD molecular determinants
Paola Paci, Giulia Fiscon, Federica Conte, Valerio Licursi, Jarrett D. Morrow, Craig P. Hersh, Michael H. Cho, Peter J. Castaldi, Kimberly Glass, Edwin K. Silverman, Lorenzo Farina
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-based analysis 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 defining given 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 well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to 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-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.