MOTCS: A Cancer Subtype Classification and Key Biomarker Recognition Model Based on Multi-Omics Data Integration of Transformer.

Cancer, as a complex disease with high heterogeneity, is gradually shifting from standardization to individualization. Current research indicates that integrating multi-omics data, including genome, transcriptome, and epigenome, can more comprehensively reveal the molecular features of tumors, thereby providing a more reliable basis for subtype classification. It is worth noting that driver genes, as key factors directly involved in tumorigenesis and development, when combined with multi-omics data, improve model interpretability. We proposed a cancer subtype classification model named MOTCS, which consists of four key steps: (1) The extraction of driver gene features from existing databases and the execution of preliminary feature selection for non-driver genes in different omics data; (2) the use of the transformer encoder to conduct feature representation learning for non-driver gene features and driver gene features, respectively; (3) the non-driver gene feature embeddings and driver gene feature embeddings of each omics learned are concatenated and input into the MLP classifier to obtain the classification probabilities of each subtype. (4) By integrating the above features through view association discovery networks, cross-omics feature representations are learned so as to achieve effective classification. The results show that MOTCS outperformed existing methods across all four datasets and that the introduction of driver genes will enhance the classification performance of MOTCS. MOTCS achieved precise classification of cancer subtypes by mining the feature embeddings of driver genes and non-driver gene features.