在结肠直肠癌中推断个性化细胞-细胞通信网络,以个性化因果发现
Aodong Qiu1,2, Binfeng Lu3, Gregory F Cooper1
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, USA.
bioRxiv : the preprint server for biology
|October 3, 2025
概括
本研究介绍了一个计算框架,用于在结直肠癌 (CRC) 中绘制个性化细胞-细胞通信网络 (CCCN). 该方法揭示了患者特定的信号模式和预后生物标志物,用于精确瘤学.
科学领域:
- 计算生物学是一种计算生物学.
- 精确瘤学是一门精确的专业.
- 癌症研究 癌症研究
背景情况:
- 瘤异质性对理解细胞-细胞通信网络 (CCCNs) 提出了挑战.
- 由于依赖人口级数据,现有的方法无法捕获患者特定的信号模式.
- 个性化的CCCN对于精密瘤学至关重要.
研究的目的:
- 开发一个整合性的计算框架来推断个性化的CCCN (iCCCN).
- 在结直肠癌 (CRC) 中识别层次结构化的基因表达模块 (GEM) 和患者特定的信号模式.
- 为了验证iCCCNs作为预后特征的临床相关性.
主要方法:
- 组合嵌套层次的迪里克莱特过程 (nHDP) 用于GEM识别与特定实例的贪快速因果推理 (iGFCI) 用于iCCCN推理.
- 将框架应用于来自超过625,000个细胞的单细胞RNA-seq数据.
- 使用TCGA批量RNA-seq数据和生存数据进行验证.
主要成果:
- 在CRC中成功分解了复杂的GEM,并在CRC中详细的细胞亚型中发现了iCCCN.
- 验证了个性化GEM因果相互作用作为预后特征的临床相关性.
- 已识别的连接体-受体对调解细胞-细胞通信,并使人们能够了解免疫逃避.
结论:
- 开发的计算框架通过实现精确的患者分层来推进个性化瘤学.
- 已识别的iCCCN和GEM为改善癌症治疗向提供了可操作的生物标志物.
- 这种方法为免疫逃避和个性化癌症治疗提供了机械的见解.
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