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.

背景：骨关节炎（OA）是一种导致高残障率和低生活质量的关节疾病。随着现代分子生物学技术的发展，已经报道了一些关键基因和诊断标记。然而，OA的病因和发病机制仍然未知。

目的：建立OA的基因特征。

方法：本研究整合了五个微阵列数据集，对OA相关基因的生物学功能进行了全面的网络和路径分析，可以提供有价值的信息，并进一步探讨OA的病因和发病机理。

结果与讨论：差异表达分析确定了180个在OA中表达明显表达的基因。功能富集分析表明，上调的基因与类风湿关节炎有关（p <0.01）。下调的基因调节激酶活性和某些信号通路如MAPK信号通路（p <0.001）和IL-17信号通路（p <0.001）的负调控的生物学过程。另外，基于差异表达的基因构建了OA特异性蛋白质-蛋白质相互作用（PPI）网络。网络拓扑属性分析表明，差异上调的VEGFA，MYC，ATF3和JUN基因是网络的中枢基因，可能通过调节细胞周期或凋亡来影响OA的发生和发展，并且是OA的潜在生物标志物。最后，使用支持向量机（SVM）方法建立OA的诊断模型，该模型不仅在内部和外部数据集中具有出色的预测能力（AUC> 0.9），而且在不同的芯片平台上具有很高的预测性能（ AUC> 0.9），并且在血样中也具有有效能力（AUC> 0.8）。

结论：4基因诊断模型可能对OA的早期诊断和预测有很大帮助。