Ping Meng

Motivation: Cancer, a disease of high complexity. Identifying cancer driver genes is fundamental for elucidating oncogenesis and promoting precision medicine. Currently, most approaches mainly focus on homogeneous gene networks and single-omics data, thereby mainly identifying coding driver genes while ignoring non-coding driver genes. Result: Thus, we introduced GMHAN, a novel framework based on HAN. Firstly, we integrated the three types of omics data of genes and PPI network topology feature, together with the multi-dimensional features of miRNAs. Afterwards, we used heterogeneous graph attention networks to obtain deep feature embeddings of genes and miRNAs. Finally, the deep feature embeddings are input multilayer perceptron to obtain the probability that genes and miRNAs being cancer drivers. In a comparative evaluation against seven methods, GMHAN demonstrates better performance across both pan-cancer and cancer-specific datasets, achieving higher scores in AUC and AUPR. It has confirmed its effectiveness in identifying carcinogenic drivers. Availability and implementation: The source code of GMHAN is available at: https://github.com/mping315/GMHAN and https://doi.org/10.5281/zenodo.20154736.

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.