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Updated: May 29, 2025
Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
Published on: March 18, 2022
Identification and Validation of Pivotal Genes in Osteoarthritis Combined with WGCNA Analysis
Chengzhuo Yang1, Xinhua Chen1, Jin Liu1
1Department of The Affiliated Rehabilitation Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Introduction:
The prevalence of osteoarthritis (OA), the most common chronic joint condition, is increasing due to the aging population and escalating obesity rates, leading to a significant impact on human health and well-being. Thus, analyzing the key targets of OA through bioinformatics can help discover new biomarkers to improve its diagnosis.
Methods:
The microarray and RNA-seq results were screened from the Gene Expression Omnibus (GEO) database. Functional enrichment analyses, protein-protein interaction (PPI) analysis, and weighted gene co-expression network analysis (WGCNA) of the DEGs were performed. RT-qPCR and WB were further performed to verify the hub gene expression in OA rat.
Results:
In this study, 35 key genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) using the GSE169077 and GSE114007 datasets. Enrichment analysis revealed that these key genes were predominantly enriched in the HIF-1 signaling pathway, ECM-receptor interaction, and FoxO signaling pathway. Through the integration of protein-protein interaction (PPI) analysis, validation in animal models and ROC curve analysis, four pivotal genes (GADD45B, CLDN5, HILPDA and CDKN1B) were finally identified.
Conclusion:
In conclusion, these identified key genes could serve as novel targets for predicting and treating OA, offering fresh insights into its etiology and pathogenesis.
