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整合机器学习模型用于使用临床和转录基因数据预测乳腺癌的整体生存率

Mehmet Kivrak¹, Hatice Sevim Nalkiran², Oguzhan Kesen³

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Recep Tayyip Erdogan University, 53020 Rize, Türkiye.

|November 27, 2025

概括

与年龄相关的基因表达变化影响Luminal A乳腺癌存活率. 整合临床和分子数据的机器学习模型为这种常见的癌症提供了卓越的预后准确性.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

乳腺癌,通常是有利的,可以受到年龄和更年期状态的影响.
传统的预后模型可能无法完全捕捉不同年龄组的生存细微差别.

研究的目的:

为了调查与Luminal A乳腺癌的年龄相关的转录组差异.
开发一个先进的预后模型,整合临床和基因组数据,以改善生存预测.

主要方法:

来自METABRIC队列的转录和临床数据的分析,根据年龄和更年期状态对患者进行分层.
使用Boruta识别差异表达基因 (DEGs) 和特征选择.
机器学习模型的培训和验证 (随机森林,物流回归,多层感知器,XGBoost) 具有交叉验证和SMOTE.

主要成果:

在不同年龄组中观察到明显的转录基因组聚类.
确定了41个与年龄和生存相关的基因,其中包括临床变量和分子标记 (例如ATM,HERC2) 的关键预测因素.
XGBoost模型实现了高性能 (精度98%,AUC 0.86),超过了其他算法.

结论:

与年龄相关的转录基因变化显著影响着Luminal A乳腺癌预后.
结合临床和分子数据的集成机器学习方法可以提高预后准确性.
这种基于ML的策略显示出在个性化乳腺癌管理中临床应用的潜力.

关键词:

在XGBoost中使用.年龄的年龄年龄的年龄.基因表达的基因表达方式光线A 乳腺癌乳腺癌机器学习是机器学习.生存预测的预测.

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