Gene Expression Profiling Analysis to Identify Key Genes and Underlying Mechanisms in Meniscus of Osteoarthritis Patients

Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage degradation, inflammatory arthritis, and joint dysfunction. Currently, there is a lack of effective early diagnostic methods and treatment strategies for OA. Bioinformatics and biomarker research provide new possibilities for early detection and personalized therapy of OA. In this study, we investigated the molecular mechanisms of OA and important signaling pathways involved in disease progression through bioinformatics analysis. Firstly, using the limma package, we analyzed the differentially expressed genes (DEGs) between normal healthy samples and OA cartilage tissue samples. These DEGs were found to be primarily involved in biological processes such as extracellular matrix (ECM) binding, immune receptor activity, and cytokine activity, as well as signaling pathways including cytokine receptors, ECM-receptor interaction, and PI3K-Akt. Gene set enrichment analysis revealed that in the OA group, signaling pathways such as AMPK, B cell receptor, IL-17, and PPAR were downregulated, while calcium signaling pathway, cell adhesion molecules, ECM-receptor interaction, TGF-β signaling pathway, and Wnt signaling pathway were upregulated. Additionally, we constructed a co-expression module network using WGCNA and identified key modules associated with OA, from which we selected 7 most predictive OA characteristic genes. Among them, ANTXR1, KCNS3, SGCD, and LIN7A were correlated with the level of immune cell infiltration. This study elucidates the mechanisms underlying the development of OA and identifies potential diagnostic markers and therapeutic targets, providing important insights for early diagnosis and treatment of OA.

Matrine From Sophora Flavescens Attenuates on Collagen-Induced Osteoarthritis by Modulating the Activity of miR-29B-3P/PGRN Axis

Matrine is an active ingredient in traditional Chinese medicine that has been shown to be effective in treating bone disorders. The anti-osteoarthritis (OA) effects of matrine were assessed using both in in vitro and in vivo systems, and the mechanisms underlying the effects were investigated by focusing on the activity of miR-29b-3p/PGRN axis. The miR was chosen as potential target for matrine after chondrocytes were treated with both IL-1? and matrine. Changes in cell viability, cell apoptosis, inflammation, and miR-29b-3p/PGRN axis were detected. In vitro assays results were validated using collagen-induced arthritis (CIA) rat models. Incubation with IL-1? reduced cell viability, induced cell apoptosis, and inhibited production of cytokines in chondrocytes, which was associated with the up-regulation of miR-29b-3p and down-regulation of PGRN. In CIA rats, matrine reduced bone destruction and weight loss in a dose-dependent manner. Matrine also reduced the systemic levels of cytokines. At the molecular level, matrine inhibited the expression of miR-29b-3p while increasing the expression of PGRN. The findings outlined in the current study showed that matrine exerted its anti-OA effects by modulating the miR-29b-3p/PGRN axis.

CircRNAs in osteoarthritis: research status and prospect

Osteoarthritis (OA) is the most common joint disease globally, and its progression is irreversible. The mechanism of osteoarthritis is not fully understood. Research on the molecular biological mechanism of OA is deepening, among which epigenetics, especially noncoding RNA, is an emerging hotspot. CircRNA is a unique circular noncoding RNA not degraded by RNase R, so it is a possible clinical target and biomarker. Many studies have found that circRNAs play an essential role in the progression of OA, including extracellular matrix metabolism, autophagy, apoptosis, the proliferation of chondrocytes, inflammation, oxidative stress, cartilage development, and chondrogenic differentiation. Differential expression of circRNAs was also observed in the synovium and subchondral bone in the OA joint. In terms of mechanism, existing studies have mainly found that circRNA adsorbs miRNA through the ceRNA mechanism, and a few studies have found that circRNA can serve as a scaffold for protein reactions. In terms of clinical transformation, circRNAs are considered promising biomarkers, but no large cohort has tested their diagnostic value. Meanwhile, some studies have used circRNAs loaded in extracellular vesicles for OA precision medicine. However, there are still many problems to be solved in the research, such as the role of circRNA in different OA stages or OA subtypes, the construction of animal models of circRNA knockout, and more research on the mechanism of circRNA. In general, circRNAs have a regulatory role in OA and have particular clinical potential, but further studies are needed in the future.

Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis

Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA.

Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA–miRNA–mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein–protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug–gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA.

Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA–miRNA–mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA–miRNA–hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA.

Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks.

Studies on the Role of circRNAs in Osteoarthritis

Objective

Provide a reference to elucidate the mechanism of circRNAs regulating osteoarthritis (OA) and the clinical treatment.

Methods

Herein, articles about circRNAs (hsa-circ) and osteoarthritis in the recent 5 years have been reviewed and the differential expression and regulatory effect of circRNAs in OA deduced. Based on these conclusions and Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the acquired circRNAs, the potential functions and interactions of circRNAs in OA and the involved signaling pathways are discussed.

Results

A total of 33 studies meeting the inclusion criteria were included in this study, and 27 circRNAs were upregulated and 8 circRNAs were downregulated in OA. A total of 31 circRNAs were finally included in the PPI, GO, and KEGG analyses. From PPI, 12 map nodes and 7 map edges were interrelated. VWF had the biggest node and edge size. From GO, VWF showed a majority of the functions. From KEGG, circRNAs are enriched in PI3K/AKT, human papillomavirus infection (HPI), and focal adhesion (FA) pathways, and VWF was involved in major pathways.

Conclusion

We found that most articles about circRNAs regulating OA in the recent 5 years focused on the mechanism, especially the absorption effect of circ-miRNA as sponges in the recent 2 years, while most of the articles about their functions addressed ECM and PI3K, AKT, and mTOR signaling pathways. Future studies might focus on the functions of circRNAs, and circRNA VWF, with preferable functions, interactions, and involvement, can be used as a biological indicator to detect OA in clinical practice.