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Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Authors :
Kristina L. Buschur
Craig Riley
Aabida Saferali
Peter Castaldi
Grace Zhang
Francois Aguet
Kristin G. Ardlie
Peter Durda
W. Craig Johnson
Silva Kasela
Yongmei Liu
Ani Manichaikul
Stephen S. Rich
Jerome I. Rotter
Josh Smith
Kent D. Taylor
Russell P. Tracy
Tuuli Lappalainen
R. Graham Barr
Frank Sciurba
Craig P. Hersh
Panayiotis V. Benos
Source :
Respiratory Research, Vol 24, Iss 1, Pp 1-12 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background Chronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment. Methods Blood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples. Results The discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS). Conclusions The identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Details

Language :
English
ISSN :
1465993X
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Respiratory Research
Publication Type :
Academic Journal
Accession number :
edsdoj.2432ac0f1e9a4ee08f8823576baf8705
Document Type :
article
Full Text :
https://doi.org/10.1186/s12931-023-02316-6