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作为类风湿关节炎自噬的潜在诊断生物标志物:一项生物信息学研究。

as a potential diagnostic biomarker for autophagy in rheumatoid arthritis: A bioinformatics study.

机构信息

The First School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China.

The No.1 Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, Yunnan, China.

出版信息

Autoimmunity. 2024 Dec;57(1):2423759. doi: 10.1080/08916934.2024.2423759. Epub 2024 Nov 5.

Abstract

This study aimed to identify genes associated with autophagy and potential diagnostic biomarkers by comparing the gene expression profiles of synovial tissues in patients with rheumatoid arthritis (RA) and healthy individuals, aiming to offer new insights for clinical treatment strategies. We used publicly available datasets to analyze differentially expressed genes (DEGs) between the synovial tissue of RA patients and healthy individuals. Then, we intersected these DEGs with autophagy-related genes to identify autophagy genes in the synovial tissue of RA patients. We further analyzed the biological processes and functions of these genes. Furthermore, we used machine learning to identify characteristic autophagy genes in RA synovial tissue. Finally, we examined the differential expression of these characteristic genes in the blood of RA patients using an external dataset. Our study identified as a potential biomarker for diagnosing RA. gene expression was downregulated in both the synovial tissue and blood of RA patients, suggesting its involvement in multiple biological processes such as local inflammation, oxidative stress, metabolic processes, and immune responses. Our findings suggest that may be a novel biomarker for the clinical diagnosis of RA and may play a crucial role in the pathogenesis of RA. The study provides new insights into the molecular mechanisms of RA and potential new therapeutic targets.

摘要

本研究旨在通过比较类风湿关节炎(RA)患者和健康个体的滑膜组织基因表达谱,鉴定与自噬相关的基因和潜在的诊断生物标志物,为临床治疗策略提供新的思路。我们使用公共可用数据集分析 RA 患者滑膜组织与健康个体之间的差异表达基因(DEGs)。然后,我们将这些 DEGs 与自噬相关基因相交,以鉴定 RA 患者滑膜组织中的自噬基因。我们进一步分析了这些基因的生物学过程和功能。此外,我们还使用机器学习来识别 RA 滑膜组织中特征性的自噬基因。最后,我们使用外部数据集检查了这些特征基因在 RA 患者血液中的差异表达。我们的研究发现, 可能是诊断 RA 的潜在生物标志物。 基因在 RA 患者的滑膜组织和血液中均下调表达,提示其参与了多种生物学过程,如局部炎症、氧化应激、代谢过程和免疫反应。我们的研究结果表明, 可能是 RA 临床诊断的一个新的生物标志物,可能在 RA 的发病机制中发挥关键作用。该研究为 RA 的分子机制和潜在的新治疗靶点提供了新的见解。

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