一个tri-omics和机器学习框架识别出预后生物标志物和毒症中的代谢特征
Xiang Li1, Gege Ke1, Yingchun Hu2
1Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, China.
Scientific reports
|January 29, 2026
概括
研究人员通过整合多omics数据,确定了TPR和ERN1作为潜在的败血症生物标志物. 这项研究将这些生物标志物与免疫代谢变化联系起来,并建议用于败血症的潜在治疗化合物.
科学领域:
- 系统生物学和生物信息学
- 基因组学和分子生物学
- 免疫学和传染病的研究
背景情况:
- 败血症是一种危及生命的疾病,其特点是宿主对感染的反应失调,缺乏特定和稳定的生物标志物.
- 目前对于败血症的诊断和预后工具是不够的,突出显示了对新型分子点的需求.
- 多omics集成提供了一个强大的方法来解开复杂的疾病,如败血症和识别潜在的生物标志物.
研究的目的:
- 建立一个综合分析框架,结合转录组学,蛋白组学,代谢组学和单细胞转录组学,以确定与败血症相关的候选生物标志物.
- 为了生成可测试的假设,用于下游的毒症机理学和转化研究.
- 通过选针对已识别的候选生物标记物的天然化合物来探索潜在的治疗干预措施.
主要方法:
- 集成的多omics数据 (转录组学,蛋白质组学,代谢组学) 使用权重基因共同表达网络分析 (WGCNA),LASSO回归和支持矢量机-递归特征消除 (SVM-RFE).
- 利用蛋白质-现象资源进行蛋白质-疾病关联分析,并进行非向的代谢学.
- 构建了联合基因代谢物分类模型,并使用ITCM数据库进行了针对候选基因 (TPR和ERN1) 的天然化合物的选.
主要成果:
- 确定了TPR (T蛋白相关) 和ERN1 (内质网膜到核1) 作为与败血症相关的关键候选生物标志物.
- 将这些生物标志物与136种差异表达的代谢物联系起来,这些代谢物主要参与糖脂和脂肪酸代谢,这表明免疫代谢重塑.
- 单细胞转录组学揭示了免疫细胞 (单细胞巨细胞,NK细胞) 中的TPR和ERN1表达,基因代谢模型显示了歧视潜力.
结论:
- 综合的多omics方法成功地确定了TPR和ERN1作为与败血症相关的候选生物标志物,与免疫代谢变化有关.
- 这项研究为进一步实验验证这些生物标志物的诊断/预后潜力提供了一个产生假设的框架.
- 探索性化合物查确定了针对TPR/ERN1的潜在天然化合物,为败血症治疗开发提供了途径.
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