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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1378809
This article is part of the Research Topic Computational Genomic and Precision Medicine View all articles

MSFN: A Multi-omics Stacked Fusion Network for Breast Cancer Survival Prediction

Provisionally accepted
Ge Zhang Ge Zhang 1,2,3Chenwei Ma Chenwei Ma 2Chaokun Yan Chaokun Yan 1,2,3Huimin Luo Huimin Luo 1,2,3*Jianlin Wang Jianlin Wang 1,2,3Wenjuan Liang Wenjuan Liang 1,2,3Junwei Luo Junwei Luo 4
  • 1 Academy for Advanced Interdisciplinary Studies, HenanUniversity, Kaifeng,Henan, China
  • 2 School of Computer and Information Engineering, Henan University, Kaifeng, Henan Province, China
  • 3 Henan Key Laboratory of BigData Analysis and Processing,HenanUniversity, Kaifeng,Henan, China
  • 4 College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China

The final, formatted version of the article will be published soon.

    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 singleomics 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. The implementation of our method is available at https://github.com/AckerMuse/MSFN.

    Keywords: deep learning, Breast cancer survival prediction, Multi-omics data, Residual graph neural network, Convolutional Neural Network, Stacking integration

    Received: 30 Jan 2024; Accepted: 22 Jul 2024.

    Copyright: © 2024 Zhang, Ma, Yan, Luo, Wang, Liang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Huimin Luo, Academy for Advanced Interdisciplinary Studies, HenanUniversity, Kaifeng,Henan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.