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机器学习和生物信息学分析以识别外围血液中的与自相关的风湿性关节炎生物标志物

Guoqi Dong¹, Hui Gao¹, Yingqi Chen¹

¹School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Frontiers in genetics

|October 2, 2023

概括

研究人员确定了三个与自相关的基因 (EEF2,HSP90AB1,TNFSF10) 作为类风湿性关节炎 (RA) 的潜在诊断生物标志物. 这些生物标志物显示出改善RA诊断和治疗策略的前景.

科学领域:

免疫学免疫学免疫学
分子生物学分子生物学
生物标志物发现发现

背景情况:

类风湿性关节炎 (RA) 是一种常见的自身免疫性疾病,其病因不明.
自在 RA 的发展中起着重要作用.
在外周血液中确定可靠的诊断生物标志物对于RA治疗至关重要.

研究的目的:

在RA患者的外周血液中探索与自相关的诊断生物标志物.
确定RA诊断和潜在治疗的新分子标.

主要方法:

使用了基因表达概况 (GSE17755) 来自基因表达综合 (GEO) 数据库.
在RA患者和健康个体之间确定了差异表达的自相关基因 (DE-ARG).
采用机器学习算法 (随机森林,SVM-RFE,LASSO) 来精确确定诊断生物标志物.
为评估诊断价值构建了一个名录模型,并使用CIBERSORT进行免疫透分析.
在多个队列中验证的生物标志物有效性和表达趋势.

主要成果:

在RA和健康个体之间确定了25个DE-ARG.
发现了三个关键的诊断生物标志物:EEF2,HSP90AB1和TNFSF10.
诺米图模型显示出极好的诊断准确性 (AUC = 0.995).
生物标志物表达与免疫细胞透和免疫因子显著相关.
在独立队列中验证的诊断疗效和表达趋势.

结论:

通过机器学习成功地确定了RA的新型自相关诊断生物标志物 (EEF2,HSP90AB1,TNFSF10).
这些生物标志物具有很高的诊断价值和临床应用潜力.
为改善类风湿性关节炎的诊断和未来治疗提供了有希望的目标.

关键词:

自自是自的过程.诊断模型诊断模型诊断模型免疫透免疫透是什么?机器学习是机器学习.类风湿性关节炎类风湿性关节炎

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