| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
Respiratory Research, Vol 18, Iss 1, Pp 1-11 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
1465-993X |
| DOI: |
10.1186/s12931-017-0558-1 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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