Liu Ye, Molchanov Vladimir, Brass David, Yang Tao
Department of Cell Biology, Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA.
Arthritis Res Ther. 2025 May 3;27(1):100. doi: 10.1186/s13075-025-03563-2.
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.
骨关节炎（OA）是一种由环境和遗传因素共同驱动的复杂疾病。鉴于其在全球的高患病率和异质性，开发有效且个性化的治疗方法至关重要。这需要识别新的疾病机制、药物靶点和生物标志物。各种组学方法已被应用于识别与OA相关的基因、信号通路和生物标志物，包括基因组学、表观基因组学、转录组学、蛋白质组学和代谢组学。这些组学研究产生了大量数据集，正在塑造OA研究领域。高分辨率方法的出现，如单细胞和空间组学技术，进一步增强了我们剖析OA微环境中分子复杂性的能力。通过整合这些多层次数据集，研究人员可以发现核心信号枢纽和疾病机制，最终促进针对OA治疗的靶向疗法和精准医学方法的发展。
