Identification of novel potential drugs for the treatment and prevention of osteoarthritis

Abstract

Objectives: Osteoarthritis (OA) is characterized by a high prevalence, poor prognosis, and a propensity to lead to disability. Despite the availability of standard therapies, they are associated with potential side effects and don't provide a complete cure for patients. Consequently, there is an urgent demand for the development of novel drugs. Method: The gene expression profiles (GSE64394, GSE178557 and GSE215039) of normal and OA chondrocytes samples were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by the ''LIMMA'' R package. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted using the R package clusterProfiler. A protein-protein (PPI) interaction network was performed to identify hub genes by using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Small molecule compounds linked to OA were predicted through the NetworkAnalyst platform. Finally, molecular docking was conducted using AutoDock and Pymol software. Results: We identified 98 DEGs primarily implicated in endochondral ossification, extracellular matrix degradation, and Wnt signaling pathways. 23 DEGs were closely associated with OA, and 10 hub genes were found to be potential drug targets for OA. Two new targeted compounds, tetrachlorodibenzodioxin (TCDD) and valproic acid (VPA), were screened. And they both exhibited strong binding affinity to their respective targets. Conclusions: Reducing exposure to TCDD could be a crucial strategy in preventing OA, and VPA has gained recognition as a novel drug candidate for OA treatment.

Keywords

• Osteoarthritis
• Bioinformatics analysis
• Differentially expressed genes
• Tetrachlorodibenzodioxin
• Valproic acid