⚡ Quick Summary
This study identifies ageing-related biomarkers in patients with osteoarthritis (OA) and metabolic syndrome (MetS) using integrated bioinformatics and machine learning techniques. The support vector machine (SVM) model demonstrated the highest accuracy in predicting disease outcomes, highlighting the potential for early diagnosis.
🔍 Key Details
- 📊 Datasets Used: OA and MetS datasets along with ageing-related genes (ARGs) from public databases.
- ⚙️ Technologies Employed: limma package, weighted gene coexpression network analysis (WGCNA), and machine learning algorithms (RF, SVM, GLM, XGB).
- 🏆 Performance Metrics: SVM model selected based on diagnostic accuracy and correlation with immune cell infiltration.
🔑 Key Takeaways
- 🔬 Identification of 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs.
- 🤖 SVM model includes key genes: CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42.
- 📈 Strong diagnostic values based on nomogram data for early diagnosis of OA in MetS patients.
- 🧬 Significant correlation found between hub ARGs and immune cell infiltration.
- 🌟 Potential for improved diagnostic strategies in clinical settings for OA and MetS.
📚 Background
Osteoarthritis (OA) is a degenerative joint disease that significantly impacts the quality of life, particularly in older adults. The interplay between OA and metabolic syndrome (MetS) is complex, with ageing being a critical factor in their pathogenesis. Understanding the genetic underpinnings of these conditions can pave the way for better diagnostic and therapeutic strategies.
🗒️ Study
The study utilized a comprehensive approach by retrieving OA and MetS datasets along with ageing-related genes from public databases. Researchers employed the limma package to identify differentially expressed genes (DEGs) and used WGCNA to screen gene modules. Various machine learning algorithms were tested to determine the most effective model for disease prediction.
📈 Results
The analysis revealed 20 intersecting genes that are crucial for understanding the relationship between OA and MetS. Among the machine learning models tested, the SVM model outperformed others, demonstrating a strong correlation with immune cell infiltration and providing robust diagnostic capabilities based on the constructed nomogram.
🌍 Impact and Implications
The findings from this study have significant implications for the early diagnosis of OA in patients with MetS. By identifying key ageing-related biomarkers, healthcare professionals can enhance diagnostic accuracy and potentially tailor treatment strategies. This research underscores the importance of integrating bioinformatics and machine learning in advancing our understanding of complex diseases.
🔮 Conclusion
This study highlights the transformative potential of bioinformatics and machine learning in the diagnosis of osteoarthritis associated with metabolic syndrome. The identification of key ageing-related genes not only aids in early diagnosis but also opens avenues for future research into targeted therapies. Continued exploration in this field is essential for improving patient outcomes and advancing healthcare practices.
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Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.
Abstract
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.
Author: [‘Huang J’, ‘Wang L’, ‘Zhou J’, ‘Dai T’, ‘Zhu W’, ‘Wang T’, ‘Wang H’, ‘Zhang Y’]
Journal: Artif Cells Nanomed Biotechnol
Citation: Huang J, et al. Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning. Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning. 2025; 53:57-68. doi: 10.1080/21691401.2025.2471762