Genomic Analysis of Cellular Senescence and Osteoporosis Target Genes and Small Molecule Screening

Dan MA; Yajuan ZHANG; Binbin MA; Qiaoning YUE; Jianping LIU

doi:10.12259/j.issn.2095-610X.S20250220

Volume 46 Issue 2

Feb. 2025

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Dan MA, Yajuan ZHANG, Binbin MA, Qiaoning YUE, Jianping LIU. Genomic Analysis of Cellular Senescence and Osteoporosis Target Genes and Small Molecule Screening[J]. Journal of Kunming Medical University, 2025, 46(2): 141-150. doi: 10.12259/j.issn.2095-610X.S20250220

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Dan MA, Yajuan ZHANG, Binbin MA, Qiaoning YUE, Jianping LIU. Genomic Analysis of Cellular Senescence and Osteoporosis Target Genes and Small Molecule Screening[J]. Journal of Kunming Medical University, 2025, 46(2): 141-150. doi: 10.12259/j.issn.2095-610X.S20250220

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Genomic Analysis of Cellular Senescence and Osteoporosis Target Genes and Small Molecule Screening

doi: 10.12259/j.issn.2095-610X.S20250220

Dan MA^{1, 2},
Yajuan ZHANG^{1, 2},
Binbin MA³,
Qiaoning YUE⁴,
Jianping LIU^{2
,
,}

1.
School of Public Health，Dali University，Dali Yunnan 671000
2.
Research Management Department; Yuxi People’s Hospital
3.
Emergency Surgery
4.
Department of Orthopaedics Surgery，Yuxi People’s Hospital ，Yuxi Yunnan 653100，China

Received Date: 2024-08-20
Available Online: 2025-01-04
Publish Date: 2025-02-18

Abstract

Abstract

Objective To explore common target genes related to aging and osteoporosis using data from public databases and to screen for small molecule compounds with potential therapeutic effects. Methods Osteoporosis datasets GSE56814 and GSE56815 were downloaded and merged from the public gene expression database （Gene Expression Omnibus （GEO）） . Senescent genes were selected and merged from the genecard database and the cell age database. Data cleaning and differential gene selection were performed using R software, and a volcano plot was created. Weighted gene co-expression network analysis was performed to screen out key modules and genes, and the machine learning methods were employed to screen target genes. A Venn diagram was drawn using the the Microbioinformatics platform, and cytoscape software was used to visualise protein interactions results. Interaction analysis of differentially encoded proteins was carried out using the STRING database, and prediction of small molecule compounds was carried out using Cmap database. Finally, Receiver operating characteristic （ROC） curves were plotted to find out the most meaningful target genes, followed by the establishment of a logistic regression model and the creation of a nomogram. Results A total of 127 differential genes were screened using the limma package. Weighted gene co-expression network analysis was performed on the differential genes and 103 highly expressed genes were screened. Three machine learning methods, lasso regression, support vector machine and random forest model, were used to finally obtain four target genes: FOXO3, HIRA, CBX5 and RAD1. The nomogram and calibration curves showed good predictive results. Five small molecule compounds with potential therapeutic effects were identified using the Cmap database: sakuraflavin （O-methylated isoflavone）, XMD-885, kangaroo hyoscyamine, everolimus and XMD-1150. Conclusion FOXO3 and HIRA genes have now been confirmed to be the common target genes for aging and osteoporosis, and sakuraflavin has potential therapeutic effects on both conditions.
- Cellular senescence,
- Osteoporosis,
- Target genes,
- Small molecule compounds,
- Genomic analysis

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References(39)

References

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