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基于XGBoost算法的高风险骨肉瘤患者预测,使用来自SGH-OS队列的转录组和甲基化数据

Weisong Zhao^1,2, Huanliang Meng^1,2, Zhenwu Dai^3,4,5

¹Department of Orthopaedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

JCO precision oncology

|March 28, 2025

概括

一个新的分类器使用基因表达和甲基化数据准确预测高风险骨髓瘤 (OS). 这种工具有助于临床医生识别预后最差的患者,从而改善治疗决策.

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:

骨髓瘤 (OS) 是一种具有高转移率和死亡率的原发性骨癌.
之前的多组学分析确定了四种OS亚型,包括一个高风险的MYC驱动的亚型,预后不佳.
需要一个诊断工具,以预防性地识别高风险的操作系统.

研究的目的:

开发一个预测高风险操作系统亚型的分类器.
利用转录组和甲基化数据进行准确的预测.
帮助临床医生早期识别高风险患者.

主要方法:

使用极端梯度提升 (XGBoost) 与贝叶斯优化开发了一个分类模型.
来自上海总医院OS (SGH-OS) 队列的综合转录组和甲基化数据.
验证了模型在外部TARGET-OS队列上的表现.

主要成果:

开发了一种XGBoost分类器,其中包含9个基因 (ARHGAP9,CADM1,CPE,DUSP3,FGFR1,GALNT3,IGF2BP3,KIF26A,ZFP3) 的基因.
该分类器取得了出色的性能:内部队列中的AUC为0.999和准确度为0.989.
在外部队列中成功分层生存结果;IGF2BP3与MYC信号相关,表明治疗目标.

结论:

开发的分类器显示了高风险操作系统的优异预测性能.
这种工具可以增强临床决策和患者管理策略.
确定IGF2BP3作为高风险OS的潜在治疗标.

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