ArticleFrontiers in pharmacology2024
Identification of biomarkers and potential drug targets in osteoarthritis based on bioinformatics analysis and mendelian randomization.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dysregulation of the glycolysis-mitochondrial metabolism axis in osteoarthritis: mechanisms and therapeutic implications.Journal of orthopaedic translation · 2026Review
- Gut microbiota metabolites drive osteosarcoma progression through JUP activation: a multi-omics mendelian randomization study.AMB Express · 2026Article
- Investigating the Biomarkers for Alzheimer's Disease: Insights from Microarray Analysis, Mendelian Randomization, and Experimental Validation.Current medicinal chemistry · 2026Article
- Proteomic ratio reveals subtype-specific genetic mechanisms and therapeutic targets in osteoarthritis.Clinical proteomics · 2025Article
- Gene Profiling Analyses of Synovium Tissues in Korean Osteoarthritis Patients.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Background: Osteoarthritis (OA) can lead to chronic joint pain, and currently there are no methods available for complete cure. Utilizing the Gene Expression Omnibus (GEO) database for bioinformatics analysis combined with Mendelian randomization (MR) has been widely employed for drug repurposing and discovery of novel therapeutic targets. Therefore, our research focus is to identify new diagnostic markers and improved drug target sites. Methods: Gene expression data from different tissues of synovial membrane, cartilage and subchondral bone were collected through GEO data to screen out differential genes. Two-sample MR Analysis was used to estimate the causal effect of expression quantitative trait loci (eQTL) on OA. Through the intersection of the two, core genes were obtained, which were further screened by bioinformatics analysis for Results: In the joint analysis utilizing the GEO database and MR approach, five genes exhibited significance across both analytical methods. These genes were subjected to bioinformatics analysis, revealing their close association with immunological functions. Further refinement identified two core genes (ARL4C and GAPDH), whose expression levels were found to decrease in OA pathology and exhibited a protective effect in the MR analysis, thus demonstrating consistent trends. Support from Conclusion: This study identified potential diagnostic biomarkers and drug targets for OA through the utilization of the GEO database and MR analysis. The findings suggest that the ARL4C and GAPDH genes may serve as therapeutic targets, offering promise for personalized treatment of OA.
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