Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning.

ABSTRACT: Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage degradation, inflammatory arthritis, and joint dysfunction. Currently, there is a lack of effective early diagnostic methods and treatment strategies for OA. Bioinformatics and biomarker research provide new possibilities for early detection and personalized therapy of OA. In this study, we investigated the molecular mechanisms of OA and important signaling pathways involved in disease progression through bioinformatics analysis. Firstly, using the limma package, we analyzed the differentially expressed genes (DEGs) between normal healthy samples and OA cartilage tissue samples. These DEGs were found to be primarily involved in biological processes such as extracellular matrix (ECM) binding, immune rec

SUBMITTER: Wang K 

PROVIDER: S-EPMC10944228 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature