Abstract
The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA). The differentially expressed genes (DEGs) in synovial membrane samples and blood samples were respectively identified from the GEO dataset. The intersection DEGs between synovial membrane and blood were further screened out, followed by the functional annotation of these common DEGs. The optimal intersection gene biomarkers for OA diagnostics were determined. The GSE51588 dataset of articular cartilage was used for expression validation and further diagnostic analysis validation of identified gene biomarkers for OA diagnostics. There were 379 intersection DEGs were obtained between the synovial membrane and blood samples of OA. 22 DEGs had a diagnostic value for OA. After further screening, a total of 9 DEGs including TLR7, RTP4, CRIP1, ZNF688, TOP1, EIF1AY, RAB2A, ZNF281 and UIMC1 were identified for OA diagnostic. The identified DEGs could be considered as potential diagnostic biomarkers for OA.
Highlights
The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA)
Synovial membrane and peripheral blood have been used for pathological analysis in end-stage osteoarthritis knee joints[8]
A total of 379 intersection differentially expressed genes (DEGs) were obtained between the synovial membrane and blood samples (Fig. 2)
Summary
The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA). The differentially expressed genes (DEGs) in synovial membrane samples and blood samples were respectively identified from the GEO dataset. The intersection DEGs between synovial membrane and blood were further screened out, followed by the functional annotation of these common DEGs. The optimal intersection gene biomarkers for OA diagnostics were determined. The GSE51588 dataset of articular cartilage was used for expression validation and further diagnostic analysis validation of identified gene biomarkers for OA diagnostics. There were 379 intersection DEGs were obtained between the synovial membrane and blood samples of OA. In order to explore the molecular mechanism and identify potential diagnostic gene biomarkers for OA, the high-throughput transcriptome data of OA from the synovial membrane and blood samples were firstly analyzed based on the GEO datasets. The intersection DEGs between synovial membrane and blood samples were further identified. The selected DEGs were verified in expression level and diagnostic capability using OA data from articular cartilage in the GEO database
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