Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.


Journal

Rheumatology (Oxford, England)
ISSN: 1462-0332
Titre abrégé: Rheumatology (Oxford)
Pays: England
ID NLM: 100883501

Informations de publication

Date de publication:
02 03 2021
Historique:
received: 06 12 2019
revised: 04 06 2020
pubmed: 5 9 2020
medline: 29 6 2021
entrez: 5 9 2020
Statut: ppublish

Résumé

To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics. 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. 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. 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.

Identifiants

pubmed: 32885253
pii: 5901307
doi: 10.1093/rheumatology/keaa391
pmc: PMC7937023
doi:

Substances chimiques

RNA, Messenger 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1166-1175

Subventions

Organisme : Versus Arthritis
ID : 22043
Pays : United Kingdom
Organisme : The Dunhill Medical Trust
ID : R476/0516
Pays : United Kingdom

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology.

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Auteurs

Rodrigo Coutinho de Almeida (R)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Ahmed Mahfouz (A)

Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
Leiden Computational Biology Center, Leiden, The Netherlands.

Hailiang Mei (H)

Sequence Analysis Support Core, Leiden University Medical Center, Leiden, The Netherlands.

Evelyn Houtman (E)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Wouter den Hollander (W)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Jamie Soul (J)

Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne, UK.

Eka Suchiman (E)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Nico Lakenberg (N)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Jennifer Meessen (J)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands.

Kasper Huetink (K)

Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands.

Rob G H H Nelissen (RGHH)

Department Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands.

Yolande F M Ramos (YFM)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Marcel Reinders (M)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
Leiden Computational Biology Center, Leiden, The Netherlands.

Ingrid Meulenbelt (I)

Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

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