Subtype-HM: A Novel Cancer Subtype Identification Method Based on Hypergraph Learning and Multi-omics Data.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Nov 18, 2025

Abstract

Cancer is a complex and lethal disease influenced by multiple factors, and accurate subtyping is crucial for personalized treatment and prognostic evaluation. Although deep learning has made progress in cancer subtype identification, existing methods still face challenges in capturing high-order biological relationships, often overlook omics-specific information, and suffer from information loss caused by conventional feature strategies. To address these challenges, we propose Subtype-HM, a novel cancer subtype identification method based on hypergraph learning and multi-omics data. We employ multi-level hypergraphs to model complex biological structures and design a hypergraph propagation network to capture both intra- and inter-omics correlations, effectively simulating high-order biological relationships. To preserve omics-specific semantics and enrich hypergraph representations, we introduce a parallel discriminator-guided attention module that extracts omics-specific features and complements the correlated representation with unique omics-specific information. Furthermore, to avoid the information loss caused by feature fusion, we propose a multi-omics contrastive entropy alignment that aligns subtype predictions across omics while retaining their unique semantics. Experimental results on TCGA cancer datasets demonstrate that Subtype-HM outperforms 14 methods in cancer subtype identification, achieving the highest average survival analysis([Formula: see text] = 5.0) and enriched clinical parameters (3.1 on average). The identified subtypes demonstrate high biological interpretability through GO and KEGG enrichment analyses.

Authors

  • Jie Wang
  • Xin Huang
    Department of ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
  • Hulin Kuang
    From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.).
  • Cheng Yan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.

Keywords

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