Abstract
Background: Osteoarthritis (OA) is one of the main causes of disability in the elderly population, accompanied by a series of underlying pathologic changes, such as cartilage degradation, synovitis, subchondral bone sclerosis, and meniscus injury. The present study aimed to identify key genes, signaling pathways, and miRNAs in knee OA associated with the entire joint components, and to explain the potential mechanisms using computational analysis.Methods: The differentially expressed genes (DEGs) in cartilage, synovium, subchondral bone, and meniscus were identified using the Gene Expression Omnibus 2R (GEO2R) analysis based on dataset from GSE43923, GSE12021, GSE98918, and GSE51588, respectively and visualized in Volcano Plot. Venn diagram analyses were performed to identify the overlapping DEGs (overlapping DEGs) that expressed in at least two types of tissues mentioned above. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and module analysis were conducted. Furthermore, qRT-PCR was performed to validate above results using our clinical specimens.Results: As a result, a total of 236 overlapping DEGs were identified, of which 160 were upregulated and 76 were downregulated. Through enrichment analysis and constructing the PPI network and miRNA-mRNA network, knee OA-related key genes, such as HEY1, AHR, VEGFA, MYC, and CXCL12 were identified. Clinical validation by qRT-PCR experiments further supported above computational results. In addition, knee OA-related key miRNAs such as miR-101, miR-181a, miR-29, miR-9, and miR-221, and pathways such as Wnt signaling, HIF-1 signaling, PI3K-Akt signaling, and axon guidance pathways were also identified. Among above identified knee OA-related key genes, pathways and miRNAs, genes such as AHR, HEY1, MYC, GAP43, and PTN, pathways like axon guidance, and miRNAs such as miR-17, miR-21, miR-155, miR-185, and miR-1 are lack of research and worthy for future investigation.Conclusion: The present informatic study for the first time provides insight to the potential therapeutic targets of knee OA by comprehensively analyzing the overlapping genes differentially expressed in multiple joint components and their relevant signaling pathways and interactive miRNAs.
Highlights
Osteoarthritis (OA) is the most common joint disease, mainly manifesting as pain, limited joint movement, and joint deformity
The present study aims at identifying key genes, signaling pathways, and miRNAs in human knee OA by comparing the preexisting gene expression profiles derived from different joint components, including articular cartilage, synovium, subchondral bone, and meniscus
The present study aims to screen out key differentially expressed genes (DEGs), their relevant signaling pathways, and interactive miRNAs in human knee OA based on bioinformatic analysis
Summary
Osteoarthritis (OA) is the most common joint disease, mainly manifesting as pain, limited joint movement, and joint deformity. During OA development, the entire joint are affected and undergo articular cartilage degeneration, osteophyte formation, subchondral sclerosis, synovitis, and meniscus degeneration, respectively, indicating the complicated and interactive OA pathogenic mechanisms (Chen et al, 2017a). Exploration of disease-modifying osteoarthritis drugs (DMOADs) aiming at alleviating OA symptoms and/or prevent structural progression have drawn much attention. The DMOADs under research and development (R&D) and/or clinical trials mainly focus on one of the OA symptoms, such as cartilage degeneration, subchondral bone remodeling, local inflammation, or joint pain, and their potential downstream targets (Latourte et al, 2020). Osteoarthritis (OA) is one of the main causes of disability in the elderly population, accompanied by a series of underlying pathologic changes, such as cartilage degradation, synovitis, subchondral bone sclerosis, and meniscus injury. The present study aimed to identify key genes, signaling pathways, and miRNAs in knee OA associated with the entire joint components, and to explain the potential mechanisms using computational analysis
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