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

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

Coutinho de Almeida, Rodrigo (Leiden University Medical Center)
Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden Computational Biology Center)
Mei, Hailiang (Leiden University Medical Center)
Houtman, Evelyn (Leiden University Medical Center)
den Hollander, Wouter (Leiden University Medical Center)
Soul, Jamie (Newcastle University, United Kingdom)
Suchiman, Eka (Leiden University Medical Center)
Nelissen, R.G.H.H. (Leiden University Medical Center)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center; Leiden Computational Biology Center)

2021

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.

cluster analyses
osteoarthritis
RNA sequencing
subtypes

http://resolver.tudelft.nl/uuid:30c1eef4-20ef-4dda-aaae-2a7207406360

https://doi.org/10.1093/rheumatology/keaa391

Rheumatology (Oxford, England), 60 (3), 1166-1175

Institutional Repository

journal article

© 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