简介：开发有效的乳腺癌生存预测模型对于乳腺癌预后至关重要。随着下一代测序技术的广泛使用，许多研究都集中在生存预测上。然而，以前的方法主要依赖于单组学数据，使用多组学数据进行生存预测仍然是一个重大挑战。方法：在本研究中，考虑到患者的相似性和多组学数据的相关性，我们提出了一种基于堆叠策略的新型多组学堆叠融合网络（MSFN）来预测乳腺癌患者的生存。 MSFN首先构建患者相似性网络（PSN），并采用残差图神经网络（ResGCN）从PSN获取相关预后信息。同时，它采用卷积神经网络（CNN）从多组学数据中获取特异性预后信息。最后，MSFN 堆叠来自这些网络的预后信息，并将其输入 AdaboostRF 进行生存预测。结果：实验结果表明，我们的方法优于几种最先进的方法，并通过 Kaplan-Meier 和 t-SNE 进行了生物学验证。版权所有 © 2024 张、马、严、罗、王、梁和罗。

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge. Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multi-omics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction. Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.Copyright © 2024 Zhang, Ma, Yan, Luo, Wang, Liang and Luo.