Expression profiling based on graph-clustering approach to determine osteoarthritis related pathway

BACKGROUND: Osteoarthritis (OA) is the most common disease of joints in adults around the world. Current available drugs to treat osteoarthritis are predominantly directed towards the symptomatic relief of pain and inflammation but they do little to reduce joint destruction. Effective prevention of the structural damage must be a key objective of new therapeutic approaches. Therefore, it is worthwhile to search for important molecular markers that hold great promise for further treatment of patients with osteoarthritis. AIM: In this study, we used a graph-clustering approach to identify gene expression profiles that distinguish OA patients from normal samples. MATERIALS AND METHODS: We performed a comprehensive gene level assessment of osteoarthritis using five osteoarthritis samples and five normal samples graph-clustering approach. RESULTS: The results showed that TNFAIP3, ATF3, PPARG, etc, have related with osteoarthritis. Besides, we further mined the underlying molecular mechanism within these differently genes. CONCLUSIONS: The results indicated tyrosine metabolism pathway and cell cycle pathway were two significant pathways, and there was evident to demonstrate them based on previous reports. We hope to provide insights into the development of novel therapeutic targets and pathways.