MoAGNN: a multi-omics hierarchical graph neural network for subtype classification and prognosis prediction in lung...

Lung adenocarcinoma (LUAD), the most common subtype of nonsmall cell lung cancer, exhibits substantial molecular heterogeneity, complicating subtype classification, progression assessment, and treatment decision-making. Advances in high-throughput sequencing enable multi-omics analysis to reveal cancer mechanisms and biomarkers, yet the high dimensionality, heterogeneity, and interrelationships of omics layers such as transcriptome, microRNA expression, methylome, and copy number variation remain challenging to integrate through conventional methods. Most existing graph-based approaches represent patients as nodes, obscuring gene-level regulatory dynamics and limiting biological interpretability. To address this, we propose the Multi-omics Hierarchical Graph Neural Network (MoAGNN), a novel architecture that represents genes as nodes, integrates four omics, and leverages graph convolution with self-attention-based graph pooling to identify informative molecular nodes, thereby enhancing predictive performance and interpretability for LUAD subtype classification, tumor staging, and prognosis prediction. Multi-omics datasets from The Cancer Genome Atlas (TCGA) were used and results showed that MoAGNN achieved a test accuracy of 0.89 for LUAD subtype classification, outperforming conventional models (Random Forest, Support Vector Machine and Multi-Layer Perceptron) as well as state-of-the-art graph-based models MoGCN, a multi-omics integration model based on graph convolutional network, and MOGLAM, an end-to-end interpretable multi-omics integration method. Furthermore, we validated the generalizability of this framework on the GSE81089 dataset, demonstrating its potential applicability to clinically relevant risk assessment. Subsequent functional enrichment and survival analyses validated the biological relevance of the key genes identified by MoAGNN, supporting their potential roles in LUAD progression, and suggesting the broader applicability of this framework in multi-omics cancer research.