COPD subtypes identified by network-based clustering of blood gene expression

Genomics
Yale ChangPeter J Castaldi

Abstract

One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study.

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Citations

Dec 6, 2017·American Journal of Respiratory Cell and Molecular Biology·Eitan Halper-StrombergPeter J Castaldi
Mar 17, 2018·The European Respiratory Journal·Chuan-Xing LiÅsa M Wheelock
Mar 16, 2019·American Journal of Respiratory Cell and Molecular Biology·Elizabeth A ReganRussell P Bowler
Aug 5, 2017·Respiratory Research·Mengyuan KanBlanca E Himes
Jan 30, 2020·American Journal of Respiratory and Critical Care Medicine·Jarrett D MorrowDawn L DeMeo
Feb 14, 2019·Annals of the American Thoracic Society·Edwin K Silverman
May 2, 2020·Respiratory Research·Ava C WilsonMerry-Lynn N McDonald
Aug 26, 2021·PloS One·Lucas A GillenwaterKaterina J Kechris
Aug 26, 2021·International Journal of Chronic Obstructive Pulmonary Disease·Yuanlong HuXianhai Chen
Oct 12, 2021·PLoS Computational Biology·Zifeng WangPeter J Castaldi

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