在质瘤中单细胞转录组和多层信号网络的综合分析揭示了瘤进展阶段
Fereshteh Fallah Atanaki1, Leila Mirsadeghi1, Mohsen Riahi Manesh2
1Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB ), University of Tehran, Tehran, Iran.
Frontiers in genetics
|November 28, 2024
概括
这项研究开发了一个网络模型,使用单细胞RNA测序数据来分析质瘤进展. 确定了RTK通路中的关键基因,有助于预测瘤阶段和存活率.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 瘤微环境 (TMEs) 是复杂的生态系统,影响癌症进展和治疗结果.
- 通过计算来建模TME相互作用和瘤进展是具有挑战性的.
研究的目的:
- 利用单细胞RNA测序 (scRNA-seq) 数据开发一个用于分析质瘤进展期间分子变化的计算框架.
- 确定关键的分子参与者和驱动质瘤进展的途径.
主要方法:
- 利用多层网络模型与scRNA-seq数据来表示生物系统层次结构.
- 在质瘤数据上进行复杂的网络分析,差异网络分析和通路丰富分析.
- 应用机器学习模型用于预测质瘤进展阶段和患者存活率.
主要成果:
- 在质瘤中确定了显著的体受体相互作用和体受体转录因子 (TF) 轴.
- 在RTK信号通路中发现了四个关键基因 (PDGFA,PDGFRA,CREB1,PLAT),这些基因对质瘤进展至关重要.
- 在预测质瘤进展和3年生存率方面达到87%的准确性和93%的AUC.
结论:
- 开发的框架提供了关于质瘤细胞机械和分子关系的见解.
- 确定了可以告知质瘤预后和治疗策略的关键分子特征.
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