AUTHOR=Zhang Ge , Ma Chenwei , Yan Chaokun , Luo Huimin , Wang Jianlin , Liang Wenjuan , Luo Junwei
TITLE=MSFN: a multi-omics stacked fusion network for breast cancer survival prediction
JOURNAL=Frontiers in Genetics
VOLUME=15
YEAR=2024
URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1378809
DOI=10.3389/fgene.2024.1378809
ISSN=1664-8021
ABSTRACT=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.