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

Ma'en Obeidat, Yunlong Nie, Virginia Chen, Casey P Shannon, Anand Kumar Andiappan, Bernett Lee, Olaf Rotzschke, Peter J Castaldi, Craig P Hersh, Nick Fishbane, Raymond T Ng, Bruce McManus, Bruce E Miller, Stephen Rennard, Peter D Paré, Don D Sin, Ma'en Obeidat, Yunlong Nie, Virginia Chen, Casey P Shannon, Anand Kumar Andiappan, Bernett Lee, Olaf Rotzschke, Peter J Castaldi, Craig P Hersh, Nick Fishbane, Raymond T Ng, Bruce McManus, Bruce E Miller, Stephen Rennard, Peter D Paré, Don D Sin

Abstract

Background: 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.

Methods: 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 (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets.

Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 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.

Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD.

Trial registration: The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960.

Keywords: Biomarker; Blood; COPD; Co-expression; FEV1; Gene expression; Transcriptome; WGCNA; mRNA.

Figures

Fig. 1
Fig. 1
Overall study design
Fig. 2
Fig. 2
Networks of GAB2, DOCK5 and DCAF16. The figure shows the networks for GAB2, DOCK5 and DCAF16 in the yellow, green and Brown modules, respectively. The genes shown are top 50 significant genes that had a FDR adjusted P value <0.05 for association with FEV1. The size of the circle is proportional to the P value on the –log10 scale (larger = smaller P value). The thickness of the edge is proportional to the topological overlap measure (TOM) identified in WGCNA
Fig. 3
Fig. 3
Preservation Zsummary of modules from discovery cohort in the replication cohort. The Y axis shows the modules vs. their corresponding Zsummary statistics on the X axis. All modules (except the grey modules) showed a strong preservation based on the threshold prescribed in Langfelder et al. [17] of a Zsummary score >10. Furthermore, the “gold” module consists of 1000 randomly selected genes that represent a sample of the whole genome, constructed for module preservation analysis. The grey module consists of genes that were not assigned to any module in the network
Fig. 4
Fig. 4
Scatter plot of module associations with FEV1 in discovery and replication cohorts. The Y axis shows the P values (−log10 scale) for FEV1 associations in the replication cohort while the X axis shows the association P values in the discovery cohort

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