Heterogeneity-Aware Hypergraph Neural Network with Dual-Path Fusion for LUAD Stage-Specific Driver Gene Mining

Lung adenocarcinoma (LUAD) exhibits stage-specific molecular evolution and significant inter-patient het-erogeneity. Many existing driver gene identification methods typically treat LUAD as homogeneous and ignore high-order biological associations. To overcome these limitations, a heterogeneity-aware hypergraph neural network framework was proposed, where (1) a Deep & Cross Network (DCN)-based feature enhancement module was first employed to capture nonlinear cross-feature interactions, (2) an improved hypergraph neural network (HGNN) with hyperedge smoothing loss was conducted to precisely capture high-order gene-patient associations, and (3) an attention-guided dual-path residual fusion module was used to balance raw multi-omics features and hypergraph-learned latent features. Experimental results show that the proposed DPR-EHGNN framework achieves AUC values larger than 0.97 across four LUAD stages, outperforming traditional machine learning methods, GNNs, and state-of-the-art tools significantly. Its predicted pathways (e.g., MAPK signaling) and driver genes (HDAC1, TRAF6, TTN, ANK2) strongly related to LUAD, providing a robust framework to decode LUAD’s dynamic evolution and support personalized therapy in precision oncology.