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将RNA编辑与多基因数据集成,改善了机器学习模型,用于预测乳腺癌患者的药物反应

Yanara A Bernal¹, Alejandro Blanco¹, Karen Oróstica²

¹Universidad del Desarrollo.

Research square

|January 7, 2025

概括

这项研究将RNA编辑数据引入人工智能模型,用于预测乳腺癌药物反应. 使用RNA编辑站点的新型非响应风险评分显示了个性化瘤治疗策略的潜力.

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:

传统的omics数据 (基因组学,转录组学) 整合到临床使用的AI模型中受到复杂性的限制.
这项研究整合了DNA突变,RNA基因表达和新型RNA编辑数据,用于药物反应预测.
专注于A>I(G) RNA编辑为预测建模提供了一个有希望的新途径.

研究的目的:

开发和评估一种人工智能模型,用于预测乳腺癌患者的药物反应.
评估将RNA编辑数据纳入预测模型的影响.
创建一个临床可翻译的非响应风险得分.

主要方法:

来自NCT02022202研究的104名乳腺癌患者的分析.
使用Random Forest,GLM和SVM进行药物反应分类模型的培训 (70%) 和测试 (30%).
利用LASSO和PCA进行特征选择,并通过各种omics数据组合评估模型性能.

主要成果:

编辑RNA数据改善或维持了预测药物反应的随机森林模型的性能.
使用LASSO特征选择,最终选择的模型获得了0.96分和0.922的AUC.
开发了一个非响应风险评分,专注于三个RNA编辑部位 (KDM4B,miRNA200/TTLL10-AS1,BEST1) 进行临床翻译.

结论:

RNA编辑具有显著的潜力,可以增强乳腺癌中药物反应的预测建模.
开发的非反应风险得分可以通过提供治疗反应的概率来帮助临床决策.
将RNA编辑数据集成到预测模型中可以推进瘤学的个性化治疗策略.

关键词:

阿达尔 (ADAR) 是一个叫做ADAR的词.编辑RNA的RNA编辑乳腺癌乳腺癌乳腺癌药物反应药物反应机器学习是机器学习.多种经济体的整合.预测预测预测预测随机的森林随机的森林

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