Multi-Omics Data Integration Clustering for Cancer Subtypes Identification Based on Motif High-Order Similarity Graph and Tensor Regularization

Background: The precise identification of cancer subtypes through the integration of multi-omics data has emerged as a key research direction in bioinformatics. Among existing multi-omics integration methods, similarity graph-based clustering algorithms have attracted widespread interest owing to their capacity to effectively characterize the association patterns between samples. However, the majority of existing methods primarily focus on first-order relationships among samples while ignoring the prevalent high-order neighborhood relationships, and fail to fully exploit the complementary information from different omics. Methods: To address these limitations, we propose an innovative multi-omics integration framework termed MHSGTR, which integrates multi-omics data by combining Motif high-order similarity graphs and tensor regularization to identify cancer subtypes. Specifically, MHSGTR introduces Motif theory to construct a high-order similarity graph and designs a high-order graph learning term to obtain a hybrid similarity that integrates both first-order and high-order information, thereby capturing the latent high-order structural information among samples. For multi-omics data integration, we employ third-order tensor regularization constraints to explore complementary information across multi-omics data, coupled with an attention module to adaptively learn omics-specific weights for constructing a consensus similarity graph. Final clusters are derived via spectral clustering. Results: Comprehensive experiments on eight TCGA cancer datasets and a case study on adrenocortical carcinoma (ACC) demonstrate that MHSGTR achieves superior clustering performance and identifies cancer subtypes with significant biological differences, showcasing its effectiveness in robust multi-omics integration.