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Osteoarthritis (OA) is a complex disorder driven by the combination of environmental and genetic factors. Given its high global prevalence and heterogeneity, developing effective and personalized treatment methods is crucial. This requires identifying new disease mechanisms, drug targets, and biomarkers. Various omics approaches have been applied to identify OA-related genes, pathways, and biomarkers, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. These omics studies have generated vast datasets that are shaping the field of OA research. The emergence of high-resolution methodologies, such as single-cell and spatial omics techniques, further enhances our ability to dissect molecular complexities within the OA microenvironment. By integrating these multi-layered datasets, researchers can uncover central signaling hubs and disease mechanisms, ultimately facilitating the development of targeted therapies and precision medicine approaches for OA treatment.
Liu et al. (Sat,) studied this question.