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类风湿性关节炎的解码:通过生物信息学和孟德尔随机化进行生物标志物识别和免疫谱分析。

Decoding rheumatoid arthritis: Biomarker identification and immune profiling via bioinformatics and Mendelian randomization.

作者信息

Shao Shoujia, Zeng Wenxing, Zhang Jingtao, Ma Luyao, Huang Feng, Jiang Ziwei

机构信息

Guangzhou University of Chinese Medicine, Guangzhou, China.

The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

出版信息

Medicine (Baltimore). 2025 Aug 22;104(34):e43872. doi: 10.1097/MD.0000000000043872.

Abstract

Rheumatoid arthritis (RA) is a prevalent autoimmune disorder that significantly reduces quality of life and imposes a substantial burden on society. This study addresses the critical gaps in current diagnostic and therapeutic modalities by aiming to identify improved biomarkers and potential therapeutic targets. Using data from 2 gene expression omnibus databases, we executed a comprehensive differential gene expression analysis integrated with Mendelian randomization. This approach employed advanced bioinformatics tools to scrutinize expression quantitative trait loci (eQTLs) and RA genome-wide association study data to pinpoint crucial genes involved in RA. The selection of these pivotal genes was strategically based on the intersection of upregulated gene expressions with eQTLs exhibiting odds ratios >1, and conversely, downregulated gene expressions aligned with eQTLs displaying odds ratios <1. Our enrichment analyses, including gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis, provided robust validation of these genes' roles, further supported by external validation from an additional gene expression omnibus dataset. The study identified 13 critical genes related to RA susceptibility, including CKAP2, GABBR1, HLA-DPA1, ST6GAL1, FCGR1A, ADCY7, MAP4K1, CD37, ERAP2, and SEMA3C, alongside protective genes. An in-depth analysis of immune cell infiltration underscored the dominant roles of M2 macrophages and CD8+ T cells in the RA immune microenvironment, highlighting their significant contributions to disease pathogenesis. By identifying novel biomarkers and elucidating the dynamic immune landscape of RA, our findings lay the groundwork for innovative therapeutic strategies. This study significantly advances our understanding of the complex genetic mechanisms underlying RA, offering insights that pave the way for targeted therapeutic interventions and further research into the molecular drivers of RA.

摘要

类风湿性关节炎(RA)是一种常见的自身免疫性疾病,会显著降低生活质量,并给社会带来沉重负担。本研究旨在通过识别改进的生物标志物和潜在治疗靶点,解决当前诊断和治疗方式中的关键差距。利用来自2个基因表达综合数据库的数据,我们进行了一项综合差异基因表达分析,并结合孟德尔随机化。这种方法采用先进的生物信息学工具来审查表达定量性状位点(eQTL)和RA全基因组关联研究数据,以确定参与RA的关键基因。这些关键基因的选择策略性地基于上调基因表达与比值比>1的eQTL的交集,反之,下调基因表达与比值比<1的eQTL对齐。我们的富集分析,包括基因本体论、京都基因与基因组百科全书和基因集富集分析,为这些基因的作用提供了有力验证,另一个基因表达综合数据集的外部验证进一步支持了这一点。该研究确定了13个与RA易感性相关的关键基因,包括CKAP2、GABBR1、HLA - DPA1、ST6GAL1、FCGR1A、ADCY7、MAP4K1、CD37、ERAP2和SEMA3C,以及保护性基因。对免疫细胞浸润的深入分析强调了M2巨噬细胞和CD8 + T细胞在RA免疫微环境中的主导作用,突出了它们对疾病发病机制的重大贡献。通过识别新的生物标志物并阐明RA动态免疫景观,我们的发现为创新治疗策略奠定了基础。这项研究显著推进了我们对RA潜在复杂遗传机制的理解,提供了见解,为靶向治疗干预以及对RA分子驱动因素的进一步研究铺平了道路。

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