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image of Integrated Bioinformatic Analysis of the Shared Molecular Mechanisms Between Osteoporosis and Aortic Stenosis

Abstract

Introduction

Osteoporosis (OP) and aortic stenosis (AS) are highly prevalent age-related disorders that frequently coexist. Epidemiological studies suggest a pathological link between OP and AS beyond age, yet the molecular mechanisms underlying this bone-vascular axis remain poorly defined. This study aimed to identify shared genes and pathways contributing to the comorbidity of OP and AS.

Methods

Publicly available AS and OP transcriptomic datasets were retrieved from the GEO database. Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were conducted to identify disease-associated genes. Candidate hub genes were screened through protein-protein interaction (PPI) network analysis using twelve network topology algorithms. High-confidence genes were obtained by intersecting candidates with AS-related genes from the Comparative Toxicogenomics Database (CTD). Independent cohorts were used to validate candidate genes, and least absolute shrinkage and selection operator (LASSO) regression was performed to assess their diagnostic potential.

Results

WGCNA revealed 665 shared genes enriched in immune and inflammatory processes, cell adhesion, and glycosaminoglycan biosynthesis. PPI network analysis identified 32 candidate hub genes, and integration with CTD yielded 15 high-confidence genes. Validation across independent datasets confirmed dysregulated expression of CD4, GZMB, and SDC1 in both AS and OP samples. ROC analysis demonstrated high diagnostic accuracy of these genes, with a combined AUC of 0.94.

Discussion

These findings highlight immune and inflammatory pathways as convergent mechanisms driving both AS and OP. The hub genes CD4, GZMB, and SDC1 participate in immune regulation and extracellular matrix remodeling, suggesting their involvement in the shared pathogenesis of skeletal and cardiovascular degeneration.

Conclusion

Integrative bioinformatics identified CD4, GZMB, and SDC1 as key genes linking OP and AS, providing potential biomarkers and therapeutic targets for managing these age-related comorbidities.

© 2025 Bentham Science Publishers
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2025-10-29
2025-12-13

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Integrated Bioinformatic Analysis of the Shared Molecular Mechanisms Between Osteoporosis and Aortic Stenosis
Bentham Science Publishers ; https://doi.org/10.2174/0109298673382922250915061033
/content/journals/cmc/10.2174/0109298673382922250915061033
/content/journals/cmc/10.2174/0109298673382922250915061033
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  • Article Type:     Research Article
Keywords: Calcific aortic stenosis ; GZMB ; bioinformatics ; CD4 ; osteoporosis ; SDC1

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