Abstract: Objective To propose a cancer subtype clustering model that integrates graph convolutional networks, self-attention mechanisms, and decoupled contrastive learning, based on multi-omics data from five cancer types in the cancer genome atlas(TCGA). Methods The model took four types of omics data from five cancer types in the TCGA database as input. For each omics type, it constructed a sample-wise relational graph and employed a graph convolutional network(GCN)to extract intra-omics structural information, thereby better preserving inter-sample feature differences. The features from different omics were concatenated and further fused through an attention mechanism, which automatically learned the relative importance and complementary relationships among omics modalities. Finally, a decoupled contrastive learning strategy was applied, and different augmented views of the same sample were used for unsupervised training, guiding the model to identify potential cancer subtypes in the absence of ground-truth labels. Results The model demonstrated good clustering performance across five cancer datasets, effectively dividing samples into distinct subtypes. In survival analysis, the survival curves of different subtypes showed significant separation, indicating that the identified subtypes were associated with different prognoses. Some subtypes also exhibited strong differentiation in clinical characteristics. Compared with several existing methods, the proposed model achieved favorable results on multiple evaluation metrics, yielding more stable clustering outcomes and demonstrating stronger biological interpretability. Conclusion This study proposes a cancer subtype clustering model that effectively integrates multi-omics data through the synergistic use of GCN, self-attention mechanisms, and contrastive learning. The model significantly improves the accuracy and clinical interpretability of cancer subtype clustering, offering a new perspective for cancer heterogeneity research and contributing to the development of personalized treatment strategies in precision medicine.

Key words: Cancer subtype clustering, Multi-omics, Graph convolutional network, Self-attention mechanism, Decoupled contrastive learning