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<p>This study assessed the effect of cellular senescence-related genes in the development of osteoporosis (OP) via the databases, which may be the potential targeted biomarkers of the OP. The development of OP is significantly influenced by cellular senescence (CS). However, the exact mechanism of CS in OP is unknown. Hence, it is imperative to uncover the molecular mechanisms and therapeutic targets implicated in CS-associated OP. In the Gene Expression Omnibus (GEO) and GeneCards databases, we identified differential genes (DEGs) that are associated with OP and CS. Subsequently, their function was assessed through GO, KEGG, and GSEA analysis. The protein-protein interaction (PPI) network was correlated and analyzed to obtain key genes. Finally, animal models were employed for experimental validation. Eight genes, CDK1, CCNB1, TOP2A, FEN1, CDC6, FOXM1, CDC25A, and MCM2, were recognized as potential biomarker genes. FEN1 and CDC6, as new potential markers, have not been reported yet. We found that cell cycle regulation has an important role in aging-induced OP, which provides new ideas for the further development of targeted therapies.</p>

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Integrated Bioinformatics Analysis and Experimental Validation of the Role of Cellular Senescence in Osteoporosis

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Peiwen Wang,
  • Xiping Hu,
  • Chunqing Han,
  • Yuanjin Chang,
  • Ruijin Xie,
  • Junxing Ye,
  • Yu Wu

摘要

This study assessed the effect of cellular senescence-related genes in the development of osteoporosis (OP) via the databases, which may be the potential targeted biomarkers of the OP. The development of OP is significantly influenced by cellular senescence (CS). However, the exact mechanism of CS in OP is unknown. Hence, it is imperative to uncover the molecular mechanisms and therapeutic targets implicated in CS-associated OP. In the Gene Expression Omnibus (GEO) and GeneCards databases, we identified differential genes (DEGs) that are associated with OP and CS. Subsequently, their function was assessed through GO, KEGG, and GSEA analysis. The protein-protein interaction (PPI) network was correlated and analyzed to obtain key genes. Finally, animal models were employed for experimental validation. Eight genes, CDK1, CCNB1, TOP2A, FEN1, CDC6, FOXM1, CDC25A, and MCM2, were recognized as potential biomarker genes. FEN1 and CDC6, as new potential markers, have not been reported yet. We found that cell cycle regulation has an important role in aging-induced OP, which provides new ideas for the further development of targeted therapies.