Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease

• Published in: Respiratory research Vol. 18; no. 1; p. 72
• Main Authors: Obeidat, Ma'en, Nie, Yunlong, Chen, Virginia, Shannon, Casey P, Andiappan, Anand Kumar, Lee, Bernett, Rotzschke, Olaf, Castaldi, Peter J, Hersh, Craig P, Fishbane, Nick, Ng, Raymond T, McManus, Bruce, Miller, Bruce E, Rennard, Stephen, Paré, Peter D, Sin, Don D
• Format: Journal Article
• Language: English
• Published: England BioMed Central 24.04.2017; BMC
• Subjects: Adult; Aged; Biological activity; Biomarkers; Biomarkers - blood; Blood; Blood cells; CD4 antigen; CD8 antigen; Chronic obstructive pulmonary disease; Co-expression; Computer Simulation; COPD; Correlation; Cytokines; Cytokines - blood; Cytokines - genetics; Deoxyribonucleic acid; DNA; Female; FEV1; Gene expression; Gene Expression Profiling - methods; Genes; Genomics; Growth factors; Humans; Interleukin 1; Interleukin 10; Interleukin 8; Lung diseases; Lymphocytes; Lymphocytes T; Male; Metabolic Networks and Pathways - genetics; Middle Aged; Models, Genetic; Modules; mRNA; Network analysis; Neutrophils; Obstructive lung disease; Peripheral blood; Preservation; Proteins; Pulmonary Disease, Chronic Obstructive - blood; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - genetics; Reproducibility of Results; Sensitivity and Specificity; Signal transduction; Transcription; Biomarker; Transcriptome; Co-expression; FEV1; mRNA; Gene expression; WGCNA; Blood; COPD
• ISSN: 1465-993X; 1465-9921; 1465-993X; 1465-9921
• DOI: 10.1186/s12931-017-0558-1

Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV ). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV . For those modules that were significantly related to FEV , we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression. Network based approaches are promising tools to identify potential biomarkers for COPD. The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960.