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

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.

Rodrigo Coutinho de Almeida, Ahmed Mahfouz, Hailiang Mei, Evelyn Houtman, Wouter den Hollander, et al. Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integrationge. Rheumatology, 2020.
@article { bib:2020_OA_clustering,
author = { Rodrigo Coutinho de Almeida and Ahmed Mahfouz and Hailiang Mei and Evelyn Houtman and Wouter den Hollander and Jamie Soul and Eka Suchiman and Nico Lakenberg and Jennifer Meessen and Kasper Huetink and Rob G H H Nelissen and Yolande F M Ramos and Marcel Reinders and Ingrid Meulenbelt },
title = { Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integrationge },
journal = { Rheumatology },
year = { 2020 },
doi = { https://doi.org/10.1093/rheumatology/keaa391 },
}
OA clustering screenshot

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