Yang C, Chen X, Liu J, Wang W, Sun L, Xie Y, Chang Q. Identification and Validation of Pivotal Genes in Osteoarthritis Combined with WGCNA Analysis.
J Inflamm Res 2025;
18:1459-1470. [PMID:
39906135 PMCID:
PMC11792882 DOI:
10.2147/jir.s504717]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2024] [Accepted: 01/21/2025] [Indexed: 02/06/2025] Open
Abstract
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.
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