Background:
Osteoarthritis (OA) is a joint disease that leads to a high disability rate
and a low quality of life. With the development of modern molecular biology techniques, some key
genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA
are still unknown.
Objective:
To develop a gene signature in OA.
Method:
In this study, five microarray data sets were integrated to conduct a comprehensive
network and pathway analysis of the biological functions of OA related genes, which can provide
valuable information and further explore the etiology and pathogenesis of OA.
Results and Discussion:
Differential expression analysis identified 180 genes with significantly
expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes
were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological
processes of negative regulation of kinase activity and some signaling pathways such as MAPK
signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA
specific protein-protein interaction (PPI) network was constructed based on the differentially
expressed genes. The analysis of network topological attributes showed that differentially upregulated
VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may
influence the occurrence and development of OA through regulating cell cycle or apoptosis, and
were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to
establish the diagnosis model of OA, which not only had excellent predictive power in internal and
external data sets (AUC > 0.9), but also had high predictive performance in different chip
platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8).
Conclusion:
The 4-genes diagnostic model may be of great help to the early diagnosis and
prediction of OA.
A Monte Carlo approach to estimate the uncertainty in soil CO
2
emissions caused by spatial and sample size variability
