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IBPGNET:基于神经网络可解释性的肺腺癌复发预测.

Zhanyu Xu1, Haibo Liao2, Liuliu Huang1

  • 1Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.

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概括

一个新的计算框架,即可解释的生物路径图神经网络 (IBPGNET),可以预测肺腺癌 (LUAD) 的复发. 该方法确定PSMC1和PSMD11作为影响复发和治疗敏感性的关键基因.

关键词:
在PSMC1和PSMD11中,PSMC1和PSMD11分别为PSMC1和PSMD11.肺部腺癌的癌症.多主题数据数据多主题数据神经网络的解释性 神经网络的解释性复发预测的复发预测

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 肺腺癌 (LUAD) 是一种流行的肺癌亚型.
  • 早期的LUAD患者在手术后面临转移性复发的显著风险 (30-50%).

研究的目的:

  • 开发一种新的计算框架,即可解释的生物路径图神经网络 (IBPGNET),用于预测LUAD复发.
  • 阐明LUAD进展和复发的基本调节机制.
  • 整合多学科数据,以提高癌症研究中的解释性.

主要方法:

  • 开发了IBPGNET,一个利用路径层次的图形神经网络模型.
  • 综合多种omics数据进行全面分析.
  • 通过对现有分类方法进行5倍交叉验证,验证了IBPGNET的性能.

主要成果:

  • 与其他分类方法相比,IBPGNET表现优越.
  • 确定PSMC1和PSMD11是与LUAD复发显著相关的基因.
  • 观察到LUAD细胞与正常细胞中的PSMC1和PSMD11表达升高.
  • 镇压PSMC1/PSMD11增强了阿法替尼的敏感性,并减少了LUAD细胞的迁移,入侵和增殖.
  • 证明PSMC1/PSMD11可以通过EGFR表达来调节治疗敏感性.

结论:

  • IBPGNET是预测LUAD复发并了解其机制的有效工具.
  • PSMC1和PSMD11是LUAD复发和治疗点的潜在生物标志物.
  • 针对PSMC1和PSMD11,可以为LUAD提供新的治疗策略,可能通过调节EGFR信号.