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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

Author(s): Ai Jiang, Peng Xu, Zhenda Zhao, Qizhao Tan, Shang Sun, Chunli Song and Huijie Leng*

Volume 23, Issue 8, 2020

Page: [805 - 813] Pages: 9

DOI: 10.2174/1386207323666200428120310

Price: $65

Abstract

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.

Keywords: Biomarker, SVM, osteoarthritis, bioinformatics, 4-genes signature, AUC.

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