Computer methods in biomechanics and biomedical engineering. 2025 Jun 11:1-13. doi: 10.1080/10255842.2025.2516767 Q41.62025
Machine learning-driven construction and validation of an intra-tumoral heterogeneity-associated prognostic model for bladder urothelial carcinoma
基于机器学习的构建和验证用于预测膀胱尿路上皮癌肿瘤内异质性相关预后的模型 翻译改进
Yiwei Gu 1 2, Hui Zhuo 1 2
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DOI: 10.1080/10255842.2025.2516767 PMID: 40500142
摘要 中英对照阅读
肿瘤内异质性（ITH）在肿瘤进展和预后中起着至关重要的作用。本研究旨在基于与ITH相关的基因构建膀胱尿路上皮癌（BLCA）的预后模型。收集并处理了来自多个公共队列的转录组学和临床数据。使用加权基因共表达网络分析识别出与ITH相关的枢纽基因。采用结合LASSO和随机生存森林算法开发了一个由14个基因组成的预后标志物。该模型在训练集和五个独立验证队列中表现出强大的预测性能，具有较高的一致性指数值和有利的时间依赖性ROC曲线。此外，单细胞RNA测序分析证实，与对照组相比，多个模型基因在BLCA样本中的表达显著上调，并且在不同类型的细胞中显示出不同的表达模式。这些发现突出了ITH相关基因的预后意义，并支持将提出的模型应用于改善BLCA患者的结局预测和指导个性化治疗。
关键词：机器学习；膀胱尿路上皮癌；肿瘤内异质性；预后。
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期刊名：Computer methods in biomechanics and biomedical engineering
缩写：COMPUT METHOD BIOMEC
ISSN：1025-5842
e-ISSN：1476-8259
IF/分区：1.6/Q4
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Machine learning-driven construction and validation of an intra-tumoral heterogeneity-associated prognostic model for bladder urothelial carcinoma