Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease
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 an...
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doaj-14821bba095a4017aeacc69c0fa6fb4d2020-11-25T00:24:07ZengBMCRespiratory Research1465-993X2017-04-0118111110.1186/s12931-017-0558-1Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary diseaseMa’en Obeidat0Yunlong Nie1Virginia Chen2Casey P. Shannon3Anand Kumar Andiappan4Bernett Lee5Olaf Rotzschke6Peter J. Castaldi7Craig P. Hersh8Nick Fishbane9Raymond T. Ng10Bruce McManus11Bruce E. Miller12Stephen Rennard13Peter D. Paré14Don D. Sin15The University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalThe University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalPrevention of Organ Failure (PROOF) Centre of ExcellencePrevention of Organ Failure (PROOF) Centre of ExcellenceSingapore Immunology NetworkSingapore Immunology NetworkSingapore Immunology NetworkChanning Division of Network Medicine, Brigham and Women’s HospitalChanning Division of Network Medicine, Brigham and Women’s HospitalThe University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalPrevention of Organ Failure (PROOF) Centre of ExcellenceThe University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalGlaxoSmithKlineDivision of Pulmonary and Critical Care Medicine, University of Nebraska Medical CenterThe University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalThe University of British Columbia Centre for Heart Lung Innovation, St Paul’s HospitalAbstract 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. SCO104960http://link.springer.com/article/10.1186/s12931-017-0558-1COPDFEV1BloodmRNAGene expressionCo-expression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease Respiratory Research COPD FEV1 Blood mRNA Gene expression Co-expression |
author_facet |
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 |
author_sort |
Ma’en Obeidat |
title |
Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
title_short |
Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
title_full |
Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
title_fullStr |
Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
title_full_unstemmed |
Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
title_sort |
network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease |
publisher |
BMC |
series |
Respiratory Research |
issn |
1465-993X |
publishDate |
2017-04-01 |
description |
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 |
topic |
COPD FEV1 Blood mRNA Gene expression Co-expression |
url |
http://link.springer.com/article/10.1186/s12931-017-0558-1 |
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