对于帕金森病的转录病理生物学和多组学预测因素
Ruifeng Hu1,2,3,4, Ruoxuan Wang1,2,3,4, Jie Yuan1,2,3,4
1APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
bioRxiv : the preprint server for biology
|July 1, 2024
概括
研究人员确定了超过1,100名帕金森氏症患者.
科学领域:
- 神经科学和遗传学 在神经科学和遗传学.
- 生物标志物发现发现
- 计算生物学 计算生物学
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.
- 大规模的转录基因数据为识别与疾病相关的分子提供了潜力.
研究的目的:
- 通过大规模RNA测序在全血中识别新的帕金森病 (PD) 相关RNA.
- 开发用于早期PD诊断和生物标志物发现的机器学习模型.
主要方法:
- 利用了来自加速医学伙伴关系在帕金森病 (AMP PD) 计划的全血总RNA测序数据.
- 在发现和验证队列中确定了重要的RNA标记物 (基因,circRNAs,eRNAs).
- 对血基RNA标记物与死后大脑多巴胺神经元中的基因表达进行了比较,并开发了一种多omics预测模型.
主要成果:
- 发现了1111个显著的PD相关RNA,包括新型循环RNA (circRNA) 和增强型RNA (eRNA).
- 在PD血液和脑神经元中确定了44个具有一致表达变化的基因,包括神经炎症标志物.
- 开发了一种多omics机器学习模型,实现了帕金森病的高诊断性能 (AUC = 0.89).
结论:
- 在血液中确定了与帕金森病相关的广泛的已知和新型RNA.
- 这些发现为早期PD检测和治疗开发提供了潜在的基于血液的生物标志物.
- 开发的计算框架支持生物标志物发现和帕金森病的早期疾病预测.
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