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Updated: Sep 19, 2025
Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
Published on: March 18, 2022
Identification and validation of aging related genes in osteoarthritis
Jian Du1,2, Tian Zhou2, Yanghui Dong2
1Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China.
Background:
Osteoarthritis (OA) is a degenerative disease associated with aging. Although an increasing body of research suggests a close relationship between aging and OA, the underlying mechanisms remain unclear. This study explores the relationship between aging related genes (ARGs) and OA, providing potential new targets for understanding the pathogenesis and treatment of OA.
Methods:
The OA synovial tissue dataset was obtained from the GEO database, and differentially expressed genes (DEGs) were screened. The DEGs were intersected with ARGs to identify differentially expressed aging related genes (DEARGs), which were then subjected to functional enrichment analysis, PPI network analysis, and machine learning algorithms (LASSO and RF) to identify key genes. In addition, a nomogram was constructed based on the key genes to predict OA risk, and its diagnostic value was evaluated using ROC curves. Subsequently, the expression levels of the key genes were validated through qRT-PCR experiments. Finally, the CIBERSORT algorithm was applied to assess the proportion of immune cells and investigate the correlation between the key genes and immune cells.
Results:
A total of 34 DEARGs were identified. PPI network analysis revealed 12 key DEARGs. Subsequently, LASSO and RF algorithms identified ATF3, KLF4, NFKBIA, and SOD2 as key genes. Based on nomogram and ROC curve analysis, these four key genes demonstrated good diagnostic value. qRT-PCR showed that ATF3, KLF4, NFKBIA, and SOD2 were significantly downregulated in OA. Immune infiltration analysis revealed differences in Plasma cells, T cells follicular helper, Mast cells resting, T cells CD4 memory resting, NK cells activated, Monocytes, and Mast cells activated between the OA group and normal controls.
Conclusion:
ATF3, KLF4, NFKBIA and SOD2 are identified as novel biomarkers associated with aging in OA and may serve as potential therapeutic targets for OA treatment.
Insights
This study identifies four key aging-related genes (ATF3, KLF4, NFKBIA, SOD2) as novel biomarkers for osteoarthritis (OA). These genes, found to be downregulated in OA, offer potential new therapeutic targets for this degenerative disease.
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Immunology
Background:
- Osteoarthritis (OA) is a degenerative joint disease closely linked to aging, but the underlying molecular mechanisms remain poorly understood.
- Aging-related genes (ARGs) are increasingly implicated in OA pathogenesis, suggesting potential therapeutic targets.
- This study investigates the interplay between ARGs and OA to uncover novel insights into disease mechanisms.
Purpose of the Study:
- To identify differentially expressed aging-related genes (DEARGs) in OA synovial tissue.
- To determine key DEARGs that can serve as biomarkers for OA risk prediction.
- To explore the correlation between identified key genes and immune cell infiltration in OA.
Main Methods:
- Differential gene expression analysis of OA synovial tissue datasets.
- Intersection of differentially expressed genes (DEGs) with aging-related genes (ARGs) to identify DEARGs.
- Functional enrichment, protein-protein interaction (PPI) network analysis, and machine learning (LASSO, RF) to identify key genes.
- Nomogram construction and ROC curve analysis for diagnostic value assessment.
- Validation of key gene expression via qRT-PCR and immune cell infiltration analysis using CIBERSORT.
Main Results:
- Identified 34 DEARGs, with 12 highlighted by PPI network analysis.
- LASSO and RF algorithms pinpointed ATF3, KLF4, NFKBIA, and SOD2 as key genes with significant diagnostic value.
- qRT-PCR confirmed downregulation of ATF3, KLF4, NFKBIA, and SOD2 in OA tissues.
- Significant differences in immune cell populations, including plasma cells and various T cell subsets, were observed between OA and normal groups.
Conclusions:
- ATF3, KLF4, NFKBIA, and SOD2 are identified as novel biomarkers associated with aging in osteoarthritis.
- These key genes represent potential therapeutic targets for osteoarthritis treatment.
- The study highlights the intricate relationship between aging, gene expression, and immune dysregulation in OA pathogenesis.
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