文章：

跨数据类型稳健聚类预测验证胶质母细胞瘤中性别与治疗反应的关联性

Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM

原文发布日期：28 January 2025

DOI: 10.3390/cancers17030445

类型: Article

开放获取: 是

英文摘要：

Background: Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these “legacy data” were used to train a predictive model capable of recapitulating this clustering in contemporary contexts. Methods: We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq. Results: The engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results. Conclusions: This work demonstrates how annotated “legacy data” can be used to build robust predictive models capable of multi-target predictions across multiple platforms.

摘要翻译： 

背景：既往研究已描述了胶质母细胞瘤中性别特异性的患者亚型分类。这些“遗留数据”所关联的聚类标签被用于训练一个预测模型，该模型能够在当代背景下重现此类聚类分析。方法：我们采用稳健的集成机器学习方法，利用基因微阵列数据训练模型，以实现跨平台预测，包括RNA测序及潜在的单细胞RNA测序。结果：构建的特征集包含多个先前报道的与患者预后相关的基因。值得注意的是，这些已知基因仅对女性患者形成了有效的预测特征，而将该预测特征应用于男性患者时产生了预期之外的结果。结论：本研究展示了如何利用带注释的“遗留数据”构建稳健的预测模型，该模型能够实现跨多个平台的多目标预测。

原文链接：

Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM