Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration
Lara D. Veeken, vol. 60, pp. 1166–1175
Abstract
OBJECTIVE: To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics. METHODS: This study includes n = 66 primary OA patients (41 knees and 25 hips), who underwent a joint replacement surgery, from which macroscopically unaffected (preserved, n = 56) and lesioned (n = 45) OA articular cartilage were collected [Research Arthritis and Articular Cartilage (RAAK) study]. Unsupervised hierarchical clustering analysis on preserved cartilage transcriptome followed by clinical data integration was performed. Protein-protein interaction (PPI) followed by pathway enrichment analysis were done for genes significant differentially expressed between subgroups with interactions in the PPI network. RESULTS: Analysis of preserved samples (n = 56) resulted in two OA subtypes with n = 41 (cluster A) and n = 15 (cluster B) patients. The transcriptomic profile of cluster B cartilage, relative to cluster A (DE-AB genes) showed among others a pronounced upregulation of multiple genes involved in chemokine pathways. Nevertheless, upon investigating the OA pathophysiology in cluster B patients as reflected by differentially expressed genes between preserved and lesioned OA cartilage (DE-OA-B genes), the chemokine genes were significantly downregulated with OA pathophysiology. Upon integrating radiographic OA data, we showed that the OA phenotype among cluster B patients, relative to cluster A, may be characterized by higher joint space narrowing (JSN) scores and low osteophyte (OP) scores. CONCLUSION: Based on whole-transcriptome profiling, we identified two robust OA subtypes characterized by unique OA, pathophysiological processes in cartilage as well as a clinical phenotype. We advocate that further characterization, confirmation and clinical data integration is a prerequisite to allow for development of treatments towards personalized care with concurrently more effective treatment response.
Authors 14
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Rodrigo Coutinho de Almeida Aachen Department of Biomedical Data Sciences Section Molecular Epidemiology
Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University · Delft University of Technology
Affiliation as printed
Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
Leiden Computational Biology Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Sequence Analysis Support Core, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne, UK
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Jennifer M. T. A. Meessen Aachen Department Orthopaedics Department of Biomedical Data Sciences Section Molecular Epidemiology
Leiden University Medical Center
Affiliation as printed
Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Marcel J. T. Reinders Aachen Department of Biomedical Data Sciences Section Molecular Epidemiology Leiden Computational Biology Centre
Leiden University · Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Leiden Computational Biology Center, Leiden, The Netherlands
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Ingrid Meulenbelt corresponding Aachen Department of Biomedical Data Sciences Section Molecular Epidemiology
Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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