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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1466825

Multi-fusion Strategy Network-guided Cancer Subtypes Discovering Based on Multi-omics Data

Provisionally accepted
Jian Liu Jian Liu 1Xinzheng Xue Xinzheng Xue 1Pengbo Wen Pengbo Wen 2Qian Song Qian Song 3*Jun Yao Jun Yao 3*Shuguang Ge Shuguang Ge 2*
  • 1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
  • 2 School of Medicine Information and Engineering , Xuzhou Medical University, Xuzhou, Jiangsu Province, China
  • 3 Taizhou Cancer Hospital, Taizhou, Zhejiang Province, China

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

    The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity. In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi--omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data. We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan-Meier survival curves showed that SMMSN can obtain cancer subtypes with significant differences. In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.

    Keywords: cancer subtypes discovering, Multi-omics data, clustering, deep learning, Fusion strategy

    Received: 18 Jul 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 Liu, Xue, Wen, Song, Yao and Ge. 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:
    Qian Song, Taizhou Cancer Hospital, Taizhou, Zhejiang Province, China
    Jun Yao, Taizhou Cancer Hospital, Taizhou, Zhejiang Province, China
    Shuguang Ge, School of Medicine Information and Engineering , Xuzhou Medical University, Xuzhou, 221004, Jiangsu Province, 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.