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在转移性前列腺癌中使用机器学习模型对基于血液的瘤相关的基因组和脂质组资料进行多组组合集成.

Shikai Fang1, Shandian Zhe2, Hui-Ming Lin3,4

  • 1University of Utah, The School of Computing, Scientific Computing and Imaging Institute, Salt Lake City, UT.

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科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 利皮多米克 (Lipidomics) 是一种消化剂.
  • 机器学习 机器学习

背景情况:

  • 晚期前列腺癌,包括转移性激素敏感 (mHSPC) 和割抗性 (mCRPC) 形式,存在复杂的预后和预测挑战.
  • 确定可靠的生物标志物来确定患者分层和治疗反应对于改善临床结果至关重要.

研究的目的:

  • 开发和验证一个多特征分类器,整合血衍生的基因组变化和脂质特征,用于预测mHSPC和mCRPC的临床结果.
  • 评估在晚期前列腺癌患者的纵向队列中使用多组方法的预后和预测准确性.

主要方法:

  • 在11年内分析了71名mHSPC和144名mCRPC患者的队列.
  • 基于等离子体的基因组变异 (120个基因) 和脂质组物种 (772) 被描述.
  • 机器学习模型,包括逻辑回归,高斯过程回归和支持矢量机器,用于构建多原子分类器.

主要成果:

  • 特定的胺 (d18:1/14:0,d18:1/17:0),CHEK2突变,AR放大和RB1删除是关键预测因素.
  • 多原子模型在mHSPC和mCRPC的各种结果预测中实现了从0.638到0.751的AUC得分.
  • 综合的多原子方法显示出优越的预测性能,与使用更少功能的模型相比.

结论:

  • 机器学习结合了多组特征,显著提高了转移性前列腺癌预测结果的准确性.
  • 这种方法对晚期前列腺癌的个性化医疗有前途.
  • 需要在独立数据集中进行进一步的验证.