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Stratification of knee osteoarthritis: two major patient subgroups identified by genome-wide expression analysis of articular cartilage
  1. Jamie Soul1,
  2. Sara L Dunn1,
  3. Sanjay Anand2,
  4. Ferdinand Serracino-Inglott3,
  5. Jean-Marc Schwartz4,
  6. Ray P Boot-Handford1,
  7. Tim E Hardingham1
  1. 1 Wellcome Trust Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  2. 2 Department of Orthopaedic Surgery, Stockport NHS Foundation Trust, Stockport, UK
  3. 3 Department of Vascular Surgery, Central Manchester NHS Foundation Trust, Manchester, UK
  4. 4 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  1. Correspondence to Professor Tim E Hardingham, Wellcome Trust Centre for Cell-Matrix Research, University of Manchester, Manchester M13 9PT, UK; tim.hardingham{at}


Introduction Osteoarthritis (OA) is a heterogeneous and complex disease. We have used a network biology approach based on genome-wide analysis of gene expression in OA knee cartilage to seek evidence for pathogenic mechanisms that may distinguish different patient subgroups.

Methods Results from RNA-Sequencing (RNA-Seq) were collected from intact knee cartilage at total knee replacement from 44 patients with OA, from 16 additional patients with OA and 10 control patients with non-OA. Results were analysed to identify patient subsets and compare major active pathways.

Results The RNA-Seq results showed 2692 differentially expressed genes between OA and non-OA. Analysis by unsupervised clustering identified two distinct OA groups: Group A with 24 patients (55%) and Group B with 18 patients (41%). A 10 gene subgroup classifier was validated by RT-qPCR in 16 further patients with OA. Pathway analysis showed increased protein expression in both groups. PhenomeExpress analysis revealed group differences in complement activation, innate immune responses and altered Wnt and TGFβ signalling, but no activation of inflammatory cytokine expression. Both groups showed suppressed circadian regulators and whereas matrix changes in Group A were chondrogenic, in Group B they were non-chondrogenic with changes in mechanoreceptors, calcium signalling, ion channels and in cytoskeletal organisers. The gene expression changes predicted 478 potential biomarkers for detection in synovial fluid to distinguish patients from the two groups.

Conclusions Two subgroups of knee OA were identified by network analysis of RNA-Seq data with evidence for the presence of two major pathogenic pathways. This has potential importance as a new basis for the stratification of patients with OA for drug trials and for the development of new targeted treatments.

  • knee osteoarthritis
  • osteoarthritis
  • inflammation
  • disease activity
  • chondrocytes

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  • Handling editor Josef S Smolen

  • Contributors TEH and RB-H designed the study. SD carried out sample collection, characterisation and RNA prep for analysis. JS and J-MS developed the analysis of the data. SA and FS-I provided clinical support for patient interaction and tissue provision. TEH, RB-H and SL drafted the paper, which was reviewed and approved by all coauthors.

  • Funding This work was supported by grants from Arthritis Research UK (20414) and the EU (SYBIL—European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 602300); The Wellcome Trust Centre for Cell-Matrix Research, University of Manchester, is supported by core funding from the Wellcome Trust (grant number 203128/Z/16/Z).

  • Competing interests None declared.

  • Ethics approval IRAS 114697.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement All data from the study are available by Public Access within the text or in online supplementary files. All RNA-seq and metadata are available at ArrayExpress and code is available at GitHub.

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