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Abstract <p>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(<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12539_2025_789_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mathrm {-log_{10}}(p)\)</EquationSource> </InlineEquation> = 5.0) and enriched clinical parameters (3.1 on average). The identified subtypes demonstrate high biological interpretability through GO and KEGG enrichment analyses.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Jie Wang,
  • Xin Huang,
  • Hulin Kuang,
  • Cheng Yan

摘要

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( \(\mathrm {-log_{10}}(p)\) = 5.0) and enriched clinical parameters (3.1 on average). The identified subtypes demonstrate high biological interpretability through GO and KEGG enrichment analyses.

Graphical Abstract