Stergiou, O.S. orcid.org/0000-0001-7734-028X, Bittner, N. orcid.org/0000-0002-8588-185X, Katsoula, G. et al. (3 more authors) (2026) Molecular clustering in osteoarthritis primary tissues identifies shared inflammatory and tissue-specific pathway profiles. Osteoarthritis and Cartilage. ISSN: 1063-4584
Abstract
Objectives
To disentangle the molecular heterogeneity of knee osteoarthritis (OA) through the classification and characterization of transcriptomic clusters in multiple joint tissues, and to uncover distinct biological pathways that will facilitate improved patient stratification.
Methods
We analyzed RNA sequencing data from 330 knee OA patients across low- and high-grade OA knee cartilage, synovium and infrapatellar fat pad tissues. We used unsupervised machine learning to identify distinct transcriptomic clusters and subsequently performed cluster-specific differential expression and pathway enrichment analyses. We applied multi-omics factor analysis in low-grade cartilage to construct a gene expression-based classifier for subtype prediction, which we validated in an independent knee OA RNA sequencing dataset.
Results
We identified robust clusters across all four joint tissues. In low-grade cartilage, we identified two patient groups separated by differences in inflammation and transcriptional regulation. A gene classifier distinguished these groups with a cross-validated accuracy of 94.5% (95% CI 91.1–96.6%). We also reproduced these subtypes in an external cohort in which the same axis similarly separated the subgroups. In high-grade cartilage there were three distinct clusters, characterized by inflammatory, neuroactive receptor-signaling, and housekeeping-transcriptional programs. Both synovium and infrapatellar fat pad showed two distinct subgroups. Despite the histological differences between these two tissues, subgrouping was based on shared biological functions related to immune activation, alongside disease tissue-specific ones.
Conclusions
Our findings identify gene expression-based patient clusters in different primary joint tissues and point to shared and disease tissue-specific molecular programs in OA, thus setting the foundation for transcription signature-based patient stratification.
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2026 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Chondrocytes; Clustering; Inflammation; Knee osteoarthritis; Osteoarthritis; Stratification |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
| Date Deposited: | 11 Sep 2026 16:27 |
| Last Modified: | 11 Sep 2026 16:27 |
| Status: | Published online |
| Publisher: | Elsevier BV |
| Refereed: | Yes |
| Identification Number: | 10.1016/j.joca.2026.08.008 |
| Related URLs: | |
| Sustainable Development Goals: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:245440 |
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Filename: PIIS1063458426010162.pdf
Licence: CC-BY 4.0


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