因果推理和注释的蛋白组学数据在多组学癌症研究中的因果推理和注释
Qun Dong1, Minjia Tan2, Yingchun Zhou3
1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Molecular & cellular proteomics : MCP
|January 10, 2025
概括
一个新的因果推断模型Phoslink,确定了蛋白质酸化和表达之间的调节联系. 这种方法有助于发现肺腺癌 (LUAD) 的关键酸盐和潜在的药物点.
科学领域:
- 蛋白质组学和生物信息学
- 癌症研究 癌症研究
- 系统生物学 系统生物学
背景情况:
- 蛋白质酸化对生物过程至关重要,其失调与疾病有关.
- 变化的蛋白质表达表明病态状态.
- 由于因果关系问题,现有的方法难以准确地将蛋白质酸化和表达联系起来.
研究的目的:
- 开发一种因果推断模型,Phoslink,用于推断蛋白质酸化和表达之间的因果影响.
- 整合先前的证据和多组学数据,以进行可靠的因果推理.
- 在肺腺癌 (LUAD) 中确定关键的调节性酸盐和潜在的药物点.
主要方法:
- 开发了一种因果推理模型Phoslink,该模型整合了先前的证据和多组学数据.
- 使用模拟场景验证了Phoslink,并将其与相关性和门德尔随机化方法进行比较.
- 应用Phoslink到肺腺癌 (LUAD) 的蛋白质组和蛋白质组数据.
主要成果:
- 链在相关性和仪表变量方法中表现出优越的性能,估计得很稳定,错误发现率更低.
- 在LUAD样本中确定了109种酸盐和310种蛋白质之间的345种因果关系.
- 发现了26个与LUAD相关的关键调节酸盐,16个仅通过因果推理,并确定了潜在的药物标.
结论:
- 链提供了一种新的方法,用于因果推理在蛋白组学.
- 确定的因果关系和调节性酸盐提供了对LUAD病原学的关键见解.
- 链方便药物标识,并加速癌症蛋白质组学数据的临床翻译.
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