多omics和机器学习识别了COVID-19的新生物标志物和治疗点
Yumei Zhou1, Pengbei Fan2, Haiyun Zhang1,3
1National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Treatment of Disease, Wangqi Academy of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in immunology
|October 20, 2025
概括
这项研究确定了四个关键的分子生物标志物 (BTD,CFL1,PIGR,SERPINA3) 与COVID-19患者的CD8+T细胞相关. 这些生物标志物显示出改善COVID-19的诊断和潜在治疗点的希望.
科学领域:
- 免疫学 免疫学 免疫学
- 基因组学就是基因组学.
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
背景情况:
- COVID-19 仍然是一个重大的全球健康威胁,需要改进诊断和治疗策略.
- 识别可靠的生物标志物和了解免疫机制对于管理COVID-19至关重要.
- 目前的诊断和治疗方法需要通过分子洞察力来增强.
研究的目的:
- 用多omics框架和机器学习来预测COVID-19的关键分子标记.
- 识别和验证COVID-19诊断和潜在治疗点的生物标志物.
- 阐明特定基因和免疫细胞子集在COVID-19病变发生过程中的作用.
主要方法:
- 单细胞RNA测序 (scRNA-seq),大量RNA测序和蛋白质组学数据的综合分析.
- 机器学习模型的应用 (随机森林,SVM-RFE) 用于生物标记物识别.
- 使用了权重基因共同表达网络分析 (WGCNA) 和分子对接 (AlphaFold).
主要成果:
- 确定BTD,CFL1,PIGR和SERPINA3与COVID-19患者的CD8+T细胞丰度有显著关联.
- 这四种基因通过ROC分析在区分COVID-19患者和健康个体方面表现出高准确度.
- 蛋白质组分析揭示了36种差异表达蛋白 (DEP),其中几个与单细胞比例有关.
结论:
- BTD,CFL1,PIGR和SERPINA3已被验证为COVID-19的潜在辅助诊断标记物.
- 这些生物标志物提供了对COVID-19中CD8+T细胞介导免疫反应的见解.
- 已识别的生物标志物也可能作为COVID-19治疗的候选治疗点.
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