使用RNA-seq数据和机器学习识别心力衰竭中的替代拼接调节模式和特征拼接因素
Jia Li1, Dingyuan Tu1, Songhua Li1
1Department of Cardiovascular Medicine, Changhai Hospital, Naval Medical University, Shanghai, China.
Heliyon
|August 22, 2024
概括
这项研究揭示了异常的替代拼接事件和参与心力衰竭 (HF) 的关键拼接因素. 这些发现为心血管疾病提供了潜在的诊断和治疗点.
科学领域:
- 心血管生物学 心血管生物学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 心力衰竭 (HF) 是心血管疾病的晚期阶段.
- 了解HF的分子机制对于开发新的治疗方法至关重要.
研究的目的:
- 为HF建立一个替代拼接监管网络.
- 为了确定涉及HF病变的关键拼接因素.
- 探索HF患者中不同的分子亚型.
主要方法:
- 来自HF患者和对照组的RNA测序 (RNA-seq) 数据的生物信息学分析.
- 使用百分比拼接值 (PSI) 识别不同调节的替代拼接事件.
- 无监督的集群用于识别患者子组.
- 建立一个拼接因子-替代拼接监管网络.
- 机器学习算法 (LASSO,SVM-RFE) 来精确确定关键拼接因素.
主要成果:
- 在3142个与高频率相关的基因中,确定了4055个异常替代拼接事件.
- 丰富性分析显示,它与PPAR信号传递,actin细胞骨调节和肌肉收缩有关.
- 发现了两种独特的HF患者群,具有独特的分子通路.
- 建立了一个监管网络,并确定了203个特定的拼接因子-替代拼接事件对.
- 确定了四个关键的拼接因子 (RBM5,ZRANB2,HnRNPF,HnRNPA0) 在高频中至关重要.
结论:
- 拼接因素和替代拼接事件在HF中起着重要作用.
- 识别的监管网络和因素为HF机制提供了洞察力.
- 这些发现为HF诊断和治疗目标提供了潜在的生物标志物.
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