Network and Pathway-Based Prioritization and Analyses of Genes Related to Chronic Obstructive Pulmonary Disease

Abstract

Chronic obstructive pulmonary disease (COPD), characterized by long-term breathing problem and poor airflow, is the fourth cause of death in the United States. Although previous studies have provided insights into the effects of genetic factors on COPD, the molecular mechanism is still unknown. Here, we proposed a network and pathway-based method for prioritization and analysis of COPD-related genes. In brief, we firstly obtained COPD-related single nucleotide polymorphisms (SNPs) from dbGaP and COPD as well as normal lung tissues gene expression profiles from Gene Expression Omnibus (GEO). Combined with protein–protein interaction pairs, the SNPs and gene expression profiles were imported into dmGWAS, a R package designed for subnetwork searching, to identify COPD-specific module. What’s more, we performed functional analysis for genes in COPD-specific module with the combination of WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) and KEGG Orthology Based Annotation System (KOBAS). A COPD-specific network module containing 812 gene nodes and 2640 interactions was obtained. While, 450 of the 812 genes were annotated by WebGestalt and/or KOBAS, which were considered as reliable COPD-related genes. The annotated genes were found to be significantly associated with immune and signal transduction processes, which is consistent with the development of COPD. Finally, 427 of the 450 annotated genes formed 1389 interaction pairs, which might contribute COPD progression. This study should be helpful for understanding COPD mechanisms and its targeted therapy.