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通过全面的转录组集成来确定类风湿性关节炎突组织中的关键途径和生物标志物

Qifan Chen¹, Chu-Song Zhou², Hanhua Wu¹

¹Division of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.

Journal of orthopaedics

|November 10, 2025

概括

这项研究确定了关键基因和关键通路,涉及类风湿性关节炎 (RA) 结膜组织. 这些发现突显了细胞因子相互作用和血细胞在RA病变发生过程中的作用,并确定了潜在的新生物标志物.

科学领域:

免疫学免疫学免疫学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

类风湿性关节炎 (RA) 是一种慢性自身免疫性疾病,会导致肌炎和关节破坏.
精确的分子机制驱动RA的突组织变化仍然不清楚.

研究的目的:

在RA突组织中识别差异表达基因 (DEGs).
为了探索关键的分子通路和免疫细胞透在RA synovium.
通过机器学习发现RA的核心诊断基因.

主要方法:

来自GEO数据库的多个转录组数据集 (微阵列和RNA-seq) 的综合分析.
功能性丰富分析 (GO,KEGG) 和基因组丰富分析 (GSEA).
使用CIBERSORT和机器学习进行基因识别的免疫细胞透评估.

主要成果:

确定了9204个在免疫过程中富含的DEGs,如白细胞迁移和细胞因子活性.
通过GSEA确认了细胞因子-细胞因子受体相互作用和化学因子信号通路的激活.
揭示了RA突中显著的血细胞透,并确定了9个核心诊断基因,包括IGHG1.1.

结论:

细胞因子-细胞因子受体相互作用,化学因子信号传递和血细胞透在RA综合性病原发生过程中至关重要.
在9个已识别的核心基因中,有8个在RA中经过实验验验证.
IGHG1是一种潜在的新生物标志物,用于RA的突性病原性.

关键词:

113 机器学习机器学习生物标志物生物标志物在IGHG1上.类风湿性关节炎 (RA) 是一种突状组织突状组织

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