Background: Osteoarthritis (OA) is the most common chronic joint disease worldwide. It is characterized by pain and limited mobility in the affected joints and may even cause disability. Effective clinical options for its prevention and treatment are still unavailable. This study aimed to identify differences in gene signatures between tissue samples from OA and normal knee joints and to explore potential gene targets for OA.
Methods: Five gene datasets, namely GSE55457, GSE55235, GSE12021, GSE10575, and GSE1919, were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R programming software. The functions of these DEGs were analyzed, and a protein-protein interaction (PPI) network was constructed. Subsequently, the most relevant biomarker genes were screened using a receiver operating characteristic (ROC) curve analysis. Finally, the expression of the protein encoded by the core gene PTHLH was evaluated in clinical samples.
Results: Eleven upregulated and 9 downregulated DEGs were shared between the five gene expression datasets. Based on the PPI network and the ROC curves of upregulated genes, PTHLH was identified as the most relevant gene for OA and was selected for further validation. Immunohistochemistry confirmed significantly higher PTHLH expression in OA tissues than in normal tissues. Moreover, similar PTHLH levels were detected in the plasma and knee synovial fluid of OA patients.
Conclusion: The bioinformatics analysis and preliminary experimental verification performed in this study identified PTHLH as a potential target for the treatment of OA.
Keywords: Differentially expressed genes; GEO database; Osteoarthritis; PTHLH; Protein–protein interaction network.