Immune cells with senescence-related transcriptional signatures orchestrate the inflammatory continuum in osteoarthritis synovium: a single-cell and machine learning study

Background: Synovitis is a key driver of osteoarthritis (OA), characterized by a chronic inflammatory microenvironment. However, the contribution of immune-cell senescence to synovial pathology and its therapeutic potential remains poorly understood. This study aims to map the senescent immune landscape and identify actionable targets for OA therapy. Methods: Single-cell RNA dataset (GSE152805) of OA synovium was used to identify immune subsets with senescence-related transcriptional signatures and subset-specific gene signatures. These gene signatures served as machine-learning features to train and validate diagnostic models in a combine dataset of 4 OA transcriptomic datasets (GSE55235, GSE55457, GSE82107, GSE169077) using Random Forest, Support Vector Machine, Gradient Boosting Machine, and eXtreme Gradient Boosting. Immunofluorescence staining and Western blotting of human synovial tissues validated macrophage-associated markers. Pseudotime trajectory and cell-cell communication analyses defined temporal dynamics and intercellular communication. Drug-gene interaction analysis and molecular docking were performed to assess therapeutic potential. Results: macrophages in OA synovium. Drug prediction suggested CHEMBL1797159 as a potential ligand for RHOB and dichloroacetate as a repurposable modulator of PDK4. Conclusion: This study characterizes the transcriptomic landscape of senescence-related features in the OA synovial microenvironment. We identify RHOB and PDK4 as key macrophage-associated therapeutic targets, providing novel insights for immunomodulatory strategies in OA treatment.