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Background <p>Osteoporosis is an age-related skeletal disorder with an increasing burden of osteoporotic fractures worldwide, it is urgent the identification of reliable molecular characteristics to prevent the progression of severe osteoporosis.</p> Methods <p>Two datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and selected the cellular senescence-related genes (SRGs). Consensus clustering analysis was performed based on differentially expressed SRGs (DE-SRGs). The functional enrichment and immune features between cellular senescence-related clusters were explored. Moreover, two machine learning algorithms were used to select the candidate biomarkers for osteoporotic diagnosis. Fifty-four clinical samples were collected and used to validate the expression levels of diagnostic biomarkers using qPCR.</p> Results <p>A total of 2,706 DEGs (1,587 upregulated and 1,119 downregulated) were identified in osteoporosis. Of these DEGs, 50 DE-SRGs were screened out for consensus clustering analysis and to select the diagnostic biomarkers for osteoporosis. Two clusters were identified that were associated with the aberrant immune cell infiltrating characteristics and immune-related biological functions. Based on random forest and support vector machine–recursive feature elimination (SVM-RFE) algorithms, PDPK1, TRIM28, and WWP1 were selected and validated as the potential diagnostic biomarkers in osteoporosis.</p> Conclusion <p>In conclusion, we comprehensively discovered the cellular senescence-related characteristics and identified three crucial diagnostic biomarkers responsible for osteoporosis.</p>

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Identification and validation of the cellular senescence-associated molecular pattern and diagnostic markers for osteoporosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月2日

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  • Tengyan Liu,
  • Jiashuang Fan,
  • Jianyun Fang,
  • Zhuan Qu,
  • Yaxin He,
  • Kai Yang,
  • Jianlin Yang,
  • Juye Zhang,
  • Dan Yang,
  • Lifen Dai

摘要

Background

Osteoporosis is an age-related skeletal disorder with an increasing burden of osteoporotic fractures worldwide, it is urgent the identification of reliable molecular characteristics to prevent the progression of severe osteoporosis.

Methods

Two datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and selected the cellular senescence-related genes (SRGs). Consensus clustering analysis was performed based on differentially expressed SRGs (DE-SRGs). The functional enrichment and immune features between cellular senescence-related clusters were explored. Moreover, two machine learning algorithms were used to select the candidate biomarkers for osteoporotic diagnosis. Fifty-four clinical samples were collected and used to validate the expression levels of diagnostic biomarkers using qPCR.

Results

A total of 2,706 DEGs (1,587 upregulated and 1,119 downregulated) were identified in osteoporosis. Of these DEGs, 50 DE-SRGs were screened out for consensus clustering analysis and to select the diagnostic biomarkers for osteoporosis. Two clusters were identified that were associated with the aberrant immune cell infiltrating characteristics and immune-related biological functions. Based on random forest and support vector machine–recursive feature elimination (SVM-RFE) algorithms, PDPK1, TRIM28, and WWP1 were selected and validated as the potential diagnostic biomarkers in osteoporosis.

Conclusion

In conclusion, we comprehensively discovered the cellular senescence-related characteristics and identified three crucial diagnostic biomarkers responsible for osteoporosis.