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

Journal article (2021)

Authors

Rodrigo Coutinho de Almeida Leiden University Medical Center
A.M.E.T.A. Mahfouz Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Ahmed Mahfouz Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Hailiang Mei Leiden University Medical Center
Evelyn Houtman Leiden University Medical Center
Wouter den Hollander Leiden University Medical Center
Jamie Soul Newcastle University
HED Suchiman Leiden University Medical Center
Eka Suchiman Leiden University Medical Center
R.G.H.H. Nelissen Leiden University Medical Center
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
M. J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Marcel J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Marcel Reinders Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
More Authors... External organisation
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2021 Rodrigo Coutinho de Almeida, A.M.E.T.A. Mahfouz, Hailiang Mei, Evelyn Houtman, Wouter den Hollander, Jamie Soul, Eka Suchiman, R.G.H.H. Nelissen, M.J.T. Reinders, More Authors
DOI: https://doi.org/10.1093/rheumatology/keaa391
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Published Date

2021

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

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.