多omics机器学习分类器和帕金森病的血液转录组签名
Xianjun Dong1, Ruifeng Hu2, Ruoxuan Wang3
1Adams Center of Parkinson's Disease Research and Department of Neurology, Yale School of Medicine, Yale University.
Research square
|June 30, 2025
概括
这项研究确定了血液中的新型RNA生物标志物,用于早期检测帕金森病 (PD). 一个机器学习模型准确地预测PD,为诊断和治疗开发提供了一个新的工具.
科学领域:
- 基因组学和生物信息学
- 神经科学是一个神经科学.
- 生物标志物发现发现
背景情况:
- 帕金森病 (PD) 的诊断和治疗需要早期检测和新的生物标志物.
- 目前的诊断方法对早期的PD缺乏敏感性.
- 识别基于血液的RNA特征可以改善早期诊断和治疗策略.
研究的目的:
- 在全血中识别与帕金森病相关的新型RNA分子 (已知基因,circRNAs,eRNAs).
- 开发和验证一个多omics机器学习模型,用于准确的PD诊断.
- 探索PD相关RNA的功能相关性及其与大脑基因表达的重叠.
主要方法:
- 利用来自加速药物伙伴关系在帕金森病 (AMP PD) 计划的大规模RNA和DNA测序数据.
- 应用微分表达式分析和机器学习 (多omics模型) 用于PD预测.
- 在独立队列 (PPMI,PDBP/BioFIND) 中验证的发现,并将血液RNA资料与人类大脑数据 (BRAINcode) 进行比较.
主要成果:
- 在PD血液中确定了874个已知的基因,783个增强型RNA (eRNA) 和35个圆形RNA (circRNA) 的显著差异表达.
- 开发了一个多omics模型,以高准确度预测PD (AUC = 0.89),在一个独立的队列中得到验证.
- 发现了参与中性粒细胞激活和TNF-α信号传递的PD相关基因;确定了44个具有血液和大脑一致表达变化的基因,包括神经炎症标志物.
结论:
- 这项研究提供了一个强大的计算框架,用于PD生物标志物开发和早期诊断,使用基于血液的RNA配置文件.
- 与PD相关的众多已知和新型RNAs在循环血液中可检测到.
- 这些发现突出了基于血液的RNA生物标志物的潜力,以推进PD研究和临床实践.
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