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综合机器学习模型预测前列腺癌诊断和生化复发风险:推进精确瘤学

Yaxuan Wang^1,2, Haixia Zhu², Jianlan Ren³

¹Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.

NPJ digital medicine

|August 16, 2025

概括

这项研究开发了机器学习模型来预测前列腺癌 (PCa) 诊断和生化复发 (BCR). 鉴定出COMP是PCa进展的关键因素,也是诊断和复发的潜在双重生物标志物.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

前列腺癌 (PCa) 是全球男性癌症的主要原因.
治疗后的生化复发 (BCR) 是一个重大挑战,因为患者的治疗结果各不相同.
准确预测PCa的诊断和预后对于有效的管理至关重要.

研究的目的:

开发和验证用于预测PCa诊断的机器学习模型.
确定与BCR相关的关键基因并开发预后模型.
调查COMP作为PCa诊断和复发的潜在生物标志物的作用.

主要方法:

权重基因同表达网络分析 (WGCNA) 来识别与BCR相关的基因.
机器学习算法 (包括LASSO) 用于模型构建.
在体外和体内实验来验证基因功能.

主要成果:

确定了16个与BCR相关的基因,与PCa预后,药物敏感性和免疫透有显著关联.
开发了强大的诊断和预后模型,具有强大的预测性能.
COMP被确定为PCa进展的关键调控因素和潜在的双重生物标志物.

结论:

机器学习模型显示出预测PCa诊断和BCR的前景.
COMP是PCa进展的关键因素,也是诊断和复发预测的潜在生物标志物.
进一步验证COMP可以改善PCa患者的管理和结果.

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