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

Respiratory Research

Research

Ani Manichaikul¹  Stephen S. Rich¹  Craig P. Hersh²  Peter Castaldi²  Aabida Saferali²  W. Craig Johnson³  Grace Zhang⁴  Panayiotis V. Benos⁵  Kristina L. Buschur⁶  Yongmei Liu⁷  Peter Durda⁸  Russell P. Tracy⁹  R. Graham Barr^1,10  Frank Sciurba^1,11  Craig Riley^1,11  Silva Kasela^1,12  Tuuli Lappalainen^1,13  Josh Smith^1,14  Francois Aguet^1,15  Kristin G. Ardlie^1,15  Kent D. Taylor^1,16  Jerome I. Rotter^1,16 

[1] Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA;Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA;Department of Biostatistics, University of Washington, Seattle, WA, USA;Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA;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, 32603, Gainesville, FL, USA;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;Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA;Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA;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;Division of General Medicine, Columbia University Medical Center, New York, NY, USA;Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA;New York Genome Center, New York, NY, USA;Department of Systems Biology, Columbia University, New York, NY, USA;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;Northwest Genome Center, University of Washington, Seattle, WA, USA;The Broad Institute of MIT and Harvard, Cambridge, MA, USA;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;

关键词: COPD;    Graphical models;    Gene expression;    Disease subtypes;   

DOI  :  10.1186/s12931-023-02316-6

 received in 2022-09-22, accepted in 2023-01-05,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundChronic 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.MethodsBlood 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.ResultsThe 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).ConclusionsThe 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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