Zhang Ge, Ma Chenwei, Yan Chaokun, Luo Huimin, Wang Jianlin, Liang Wenjuan, Luo Junwei
Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, Henan, China.
School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
Front Genet. 2024 Aug 2;15:1378809. doi: 10.3389/fgene.2024.1378809. eCollection 2024.
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. 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. Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.
开发有效的乳腺癌生存预测模型对乳腺癌预后至关重要。随着下一代测序技术的广泛应用，众多研究聚焦于生存预测。然而，以往方法主要依赖单组学数据，利用多组学数据进行生存预测仍是一项重大挑战。在本研究中，考虑到患者的相似性和多组学数据的相关性，我们基于堆叠策略提出了一种新型的多组学堆叠融合网络（MSFN）来预测乳腺癌患者的生存情况。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中获取相关的预后信息。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将这些网络的预后信息进行堆叠，并输入到AdaboostRF中进行生存预测。实验结果表明，我们的方法优于几种先进方法，并通过Kaplan-Meier和t-SNE进行了生物学验证。
