Promising Novel Biomarkers and Candidate Drugs or Herbs in Osteoarthritis: Evidence from Bioinformatics Analysis of High-throughput Data

Background: The most common joint illness is osteoarthritis (OA). The goal of this study was to find changes in gene signatures between normal knee joints and OA tissue samples and look for prospective gene targets for OA. Methods: The gene expression profiles of GSE12021, GSE51588, and GSE55457 were downloaded from Gene Expression Omnibus (GEO). A total of 64 samples (40 OA and 24 standard control samples) were used. The limma program was used to find differentially expressed genes (DEGs) in OA versus NC. Functional annotation and protein-protein interaction (PPI) network construction of OA-specific DEGs were performed. Finally, the candidate drugs and herbs as potential drugs to treat OA were predicted in the DGIdb and TCMIO databases. Results: A total of 19 upregulated and 27 downregulated DEGs between OA and NC samples were identified. DEGs, such as PTN, COMP, NELL1, and MN1, have shown a significant correlation with OA and are expected to become new biomarkers. Cellular senescence, positive regulation of ossification, and Vascular endothelial growth factor (VEGF) were significantly enriched for OA-specific DEGs. In cell composition analysis, DEGs were also found to be highly enriched in the cytosol. We identified a total of 68 types of drugs or molecular compounds that are promising to reverse OA-related DEGs. Honeycomb and cinnamon oil have the possibility of treating OA. Conclusion: Our findings suggest new biomarkers that can be used to diagnose OA. Furthermore, we tried to find drugs and traditional Chinese medicine that may improve the progress of OA. This research may improve the identification and treatment of these uncontrollable chronic diseases.