MultCPM: a multi-omics cancer recurrence prediction model utilizing a multi-head attention mechanism

Deep learning-based approaches for integrating multi-omics data offer a novel perspective on cancer recurrence prediction. However, existing methods struggle to manage the complex relationships within multi-omics data and the intrinsic correlations between samples, leading to suboptimal prediction accuracy. To tackle these challenges, we propose a multi-omics cancer recurrence prediction model (MultCPM), which employs a multi-head attention mechanism to extract key information from biological pathways. Integrated with a hierarchical fusion module, the model performs layered integration of omics data to effectively capture their interdependence. Ultimately, the fused information is consolidated into a unified feature matrix, refining critical features and their relationships across omics. Results from 5-fold cross-validation, repeated five times on Breast Cancer (BRCA), Bladder Cancer (BLCA), and Liver Cancer (LIHC) datasets, demonstrate that the MultCPM model achieves superior prediction performance and robustness. Additionally, Deep SHapley Additive exPlanations (DeepSHAP) was utilized to analyze the model’s interpretability, revealing key genes closely associated with cancer recurrence, thus providing valuable insights for biological research and the development of cancer recurrence prediction algorithms. The code is publicly available at https://github.com/dowell2016/MultCPM .