Comprehensive Summary
Although osteoarthritis is a chronic and progressive musculoskeletal disease, diagnostic and therapeutic options continue to fall short. By way of integrating bioinformatics with experimental techniques, this research endeavors to push forward the diagnosis and treatment of osteoarthritis. As a component of this investigation, researchers leveraged machine learning to scrutinize gene expression data from osteoarthritic tissues, uncovering hub genes. Following this, an artificial neural network was developed for diagnostic modeling, followed by molecular docking and dynamics simulations to identify candidate therapeutics, with validation achieved via in vitro assays and Mendelian randomization. Researchers identified IRAK3, ITGBL1, and RHOU as critical hub genes in OA and developed an ANN diagnostic model achieving an AUC of 0.990. Molecular docking revealed progesterone as a potent binder to these proteins. Treatment of inflamed chondrocytes with progesterone reversed gene suppression and mitigated OA markers. Mendelian randomization further confirmed that increased progesterone levels causally reduce OA risk, especially in knee and hip joints.
Outcomes and Implications
Given that OA represents a significant contributor to global disability, and considering the limitations of existing diagnostic methods and the absence of effective medical treatments, this study addresses a pressing unmet clinical need. Findings indicate that progesterone may function as a disease-modifying therapy to halt or slow OA progression. The study’s clinical relevance is underscored by the identification of new biomarkers and a therapeutic candidate. However, the authors caution that additional in vivo and clinical studies are required prior to implementation, with no clear timeline established.