使用机器学习模型识别人类类风湿性关节炎中与菌相关的生物标志物
Jiayi Chen1, Zuhai Huang1, Chengyu Qin1
1Department of Spinal Orthopedics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Artificial cells, nanomedicine, and biotechnology
|July 17, 2025
概括
这项研究揭示了线粒.
科学领域:
- 免疫学 免疫学 免疫学
- 遗传学 遗传学 是一个
- 生物化学 生物化学
背景情况:
- 类风湿性关节炎 (RA) 是一种系统性免疫媒介疾病,导致关节损伤.
- 线粒细胞在RA病变发生过程中的作用尚不清楚.
- 在骨代谢障碍中,线粒是关键的.
研究的目的:
- 为了研究 mitophagy 在类风湿性关节炎中的作用.
- 在RA患者中识别差异表达的线粒细胞相关基因 (DE-MRGs).
- 评估DE-MRGs对RA的诊断价值.
主要方法:
- 来自基因表达综合 (GEO) 的GSE77298数据集的分析.
- 鉴定差异表达基因 (DEGs) 和与髓相关的基因 (MRGs).
- 权重基因共同表达网络分析 (WGCNA),接收器操作特征 (ROC) 曲线和人工神经网络 (ANN) 模型构建.
主要成果:
- 在GSE77298数据集中确定了267个DEG和63个DE-MRG.
- 发现TMEM45A和ZBTB25作为具有高诊断精度 (AUC 0.991和0.911) 的关键枢纽基因.
- 开发了一个名录模型,证明了RA的显著诊断价值.
结论:
- 线粒是类风湿性关节炎进展的关键因素.
- TMEM45A和ZBTB25是早期RA诊断的潜在生物标志物.
- 这些发现可能会促进RA的新型治疗策略.
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