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Background <p>Osteoporosis (OP) is a prevalent progressive metabolic bone disease in the middle-aged and elderly population. Previous research has indicated that physiological or pathological hypoxia may disrupt bone metabolism, ultimately contributing to OP development. However, the specific pathophysiological mechanisms remain unknown. Ferroptosis is a novel iron-dependent non-apoptotic cell death pathway. The objective of this investigation was to use bioinformatics analysis to identify key hub genes related to ferroptosis that may be linked to hypoxia and osteoporosis. Subsequently, we utilized experimental methods to validate these findings. </p> Methods <p>Differentially expressed genes (DEGs) in bone marrow mesenchymal stem cells (BMSCs) from primary osteoporosis patients and hypoxia-treated BMSCs were screened from the Gene Expression Omnibus (GEO) database. These DEGs were then intersected with the ferroptosis dataset (FerrDb), resulting in FerrDEGs. The potential functional roles of FerrDEGs were investigated through gene ontology (GO) and pathway enrichment analysis (e.g., KEGG, WikiPathways and Reactome). The STRING database was used to analyze protein-protein interaction networks, pinpointing hub genes within FerrDEGs. To further assess the predictive value of these hub genes, we conducted ROC analysis using the data from GSE230665 to identify key hub genes. Subsequently, NetworkAnalyst was employed to establish networks between target microRNAs(miRNAs) and key hub genes. Finally, we validated the key hub genes and miRNAs in an OVX rat model for further confirmation.</p> Results <p>A total of 50 FerrDEGs were identified in the overlap of hypoxia, OP, and ferroptosis pathways. These FerrDEGs were primarily associated with biological processes related to cellular response to chemical stress and oxidative stress. Pathway enrichment analysis revealed that these FerrDEGs played significant roles in processes such as viral carcinogenesis and epstein-barr virus infection. Multiple algorithms (CytoHubba, CytoNCA and Analyze Network) were employed to identify eight hub genes within FerrDEGs, including TP53, JUN​, SQSTM1, STAT3​, CDKN1A​​​, CAV1, CD44 and TGFBR1. Five key hub genes (JUN, SQSTM1, STAT3, CD44 and TGFBR1) were further screened by ROC analysis. Subsequently, hsa-miR-20a-5p was identified as a potential key microRNA through the construction of the miRNA-gene network. The five key hub genes and rno-miR-20a-5p were further validated in the OVX rat model.</p> Conclusion <p>In summary, we first screened ferroptosis-related key hub genes linking hypoxia and osteoporosis. The findings suggest that JUN, SQSTM1​, STAT3​ ​, CD44​​ and TGFBR1 are significantly associated with OP and hypoxia, potentially serving as biomarkers for diseases linked to ferroptosis. Additionally, hsa-miR-20a-5p was identified as a crucial upstream regulator likely involved in the regulation of these genes simultaneously.</p>

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Identification and validation of ferroptosis-related key hub genes linking hypoxia and osteoporosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Yifan Gu,
  • Qingsong Gu,
  • Yiheng Li,
  • Yuhu Chen,
  • Yicong Wang,
  • Jianqiao Li,
  • Linhui Wang,
  • Ziru Wang,
  • Min Yang

摘要

Background

Osteoporosis (OP) is a prevalent progressive metabolic bone disease in the middle-aged and elderly population. Previous research has indicated that physiological or pathological hypoxia may disrupt bone metabolism, ultimately contributing to OP development. However, the specific pathophysiological mechanisms remain unknown. Ferroptosis is a novel iron-dependent non-apoptotic cell death pathway. The objective of this investigation was to use bioinformatics analysis to identify key hub genes related to ferroptosis that may be linked to hypoxia and osteoporosis. Subsequently, we utilized experimental methods to validate these findings.

Methods

Differentially expressed genes (DEGs) in bone marrow mesenchymal stem cells (BMSCs) from primary osteoporosis patients and hypoxia-treated BMSCs were screened from the Gene Expression Omnibus (GEO) database. These DEGs were then intersected with the ferroptosis dataset (FerrDb), resulting in FerrDEGs. The potential functional roles of FerrDEGs were investigated through gene ontology (GO) and pathway enrichment analysis (e.g., KEGG, WikiPathways and Reactome). The STRING database was used to analyze protein-protein interaction networks, pinpointing hub genes within FerrDEGs. To further assess the predictive value of these hub genes, we conducted ROC analysis using the data from GSE230665 to identify key hub genes. Subsequently, NetworkAnalyst was employed to establish networks between target microRNAs(miRNAs) and key hub genes. Finally, we validated the key hub genes and miRNAs in an OVX rat model for further confirmation.

Results

A total of 50 FerrDEGs were identified in the overlap of hypoxia, OP, and ferroptosis pathways. These FerrDEGs were primarily associated with biological processes related to cellular response to chemical stress and oxidative stress. Pathway enrichment analysis revealed that these FerrDEGs played significant roles in processes such as viral carcinogenesis and epstein-barr virus infection. Multiple algorithms (CytoHubba, CytoNCA and Analyze Network) were employed to identify eight hub genes within FerrDEGs, including TP53, JUN​, SQSTM1, STAT3​, CDKN1A​​​, CAV1, CD44 and TGFBR1. Five key hub genes (JUN, SQSTM1, STAT3, CD44 and TGFBR1) were further screened by ROC analysis. Subsequently, hsa-miR-20a-5p was identified as a potential key microRNA through the construction of the miRNA-gene network. The five key hub genes and rno-miR-20a-5p were further validated in the OVX rat model.

Conclusion

In summary, we first screened ferroptosis-related key hub genes linking hypoxia and osteoporosis. The findings suggest that JUN, SQSTM1​, STAT3​ ​, CD44​​ and TGFBR1 are significantly associated with OP and hypoxia, potentially serving as biomarkers for diseases linked to ferroptosis. Additionally, hsa-miR-20a-5p was identified as a crucial upstream regulator likely involved in the regulation of these genes simultaneously.