Cheng Feng, Li Mengying, Hua Haotian, Zhang Ruikun, Zhu Yiwen, Zhu Yingjia, Zhang Yang, Tong Peijian
The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Department of Orthopedics, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Front Pharmacol. 2024 Aug 29;15:1439289. doi: 10.3389/fphar.2024.1439289. eCollection 2024.
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.
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 and molecular experimental verification. Finally, drug prediction and molecular docking further verified the medicinal value of drug targets.
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 and molecular experiments was also obtained, while molecular docking revealed favorable interactions between the drugs and proteins, in line with existing structural data.
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.
骨关节炎（OA）可导致慢性关节疼痛，目前尚无完全治愈的方法。利用基因表达综合数据库（GEO）进行生物信息学分析并结合孟德尔随机化（MR）已广泛应用于药物再利用和新治疗靶点的发现。因此，我们的研究重点是识别新的诊断标志物和改进药物靶点。
通过GEO数据收集滑膜、软骨和软骨下骨不同组织的基因表达数据，以筛选差异基因。采用两样本MR分析估计表达数量性状位点（eQTL）对OA的因果效应。通过两者的交集获得核心基因，再通过生物信息学分析和分子实验验证进行进一步筛选。最后，药物预测和分子对接进一步验证了药物靶点的药用价值。
在利用GEO数据库和MR方法的联合分析中，有五个基因在两种分析方法中均表现出显著性。对这些基因进行生物信息学分析，发现它们与免疫功能密切相关。进一步筛选确定了两个核心基因（ARL4C和GAPDH），其表达水平在OA病理过程中降低，且在MR分析中显示出保护作用，呈现出一致的趋势。同时也获得了分子实验的支持，而分子对接显示药物与蛋白质之间存在良好的相互作用，与现有结构数据相符。
本研究通过利用GEO数据库和MR分析，确定了OA潜在的诊断生物标志物和药物靶点。研究结果表明，ARL4C和GAPDH基因可能作为治疗靶点，为OA的个性化治疗提供了希望。
