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Tytuł pozycji:

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

Tytuł:
Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis.
Autorzy:
Buschur KL; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA.; Division of General Medicine, Columbia University Medical Center, New York, NY, USA.; New York Genome Center, New York, NY, USA.
Riley C; Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Saferali A; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Castaldi P; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Zhang G; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Aguet F; The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ardlie KG; The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Durda P; Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Craig Johnson W; Department of Biostatistics, University of Washington, Seattle, WA, USA.
Kasela S; New York Genome Center, New York, NY, USA.; Department of Systems Biology, Columbia University, New York, NY, USA.
Liu Y; Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA.
Manichaikul A; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Rich SS; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Rotter JI; The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Smith J; Northwest Genome Center, University of Washington, Seattle, WA, USA.
Taylor KD; The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Tracy RP; Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.; Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Lappalainen T; New York Genome Center, New York, NY, USA.; Department of Systems Biology, Columbia University, New York, NY, USA.; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Graham Barr R; Division of General Medicine, Columbia University Medical Center, New York, NY, USA.
Sciurba F; Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Hersh CP; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Benos PV; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. .; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA. .; Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32603, USA. .
Źródło:
Respiratory research [Respir Res] 2023 Jan 25; Vol. 24 (1), pp. 30. Date of Electronic Publication: 2023 Jan 25.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Publication: 2001- : London : BioMed Central Ltd.
Original Publication: London : Current Science Ltd., c2000-
MeSH Terms:
Gene Regulatory Networks*/genetics
Pulmonary Disease, Chronic Obstructive*/diagnosis
Pulmonary Disease, Chronic Obstructive*/genetics
Humans ; Smokers ; Genome-Wide Association Study/methods ; Prognosis
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Grant Information:
T32 HL144442 United States HL NHLBI NIH HHS; R01HL159805 United States NH NIH HHS; K01 HL157613 United States HL NHLBI NIH HHS; R01HL121270 United States NH NIH HHS; R01 HL142028 United States HL NHLBI NIH HHS; R01 HL157879 United States HL NHLBI NIH HHS; R01HL125583 United States NH NIH HHS; R01HL142028 United States NH NIH HHS; R01 HL159805 United States HL NHLBI NIH HHS; T32HL144442 United States NH NIH HHS; R01 HL121270 United States HL NHLBI NIH HHS
Contributed Indexing:
Keywords: COPD; Disease subtypes; Gene expression; Graphical models
Entry Date(s):
Date Created: 20230125 Date Completed: 20230127 Latest Revision: 20231003
Update Code:
20240105
PubMed Central ID:
PMC9875487
DOI:
10.1186/s12931-023-02316-6
PMID:
36698131
Czasopismo naukowe
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.
(© 2023. The Author(s).)

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