Integrating multiomics data using a correlation based graph attention network for subtype classification in lower...

Accurate classification of cancer subtypes is crucial for personalised therapies and targeted interventions. In this study, we propose BioGAT-LGG, a deep learning framework that integrates multi-omics data, including mRNA, miRNA, and DNA methylation, using a correlation-based Graph Attention Network version 2 (GATv2) for biomarker discovery and Lower-Grade Glioma (LGG) subtype classification. Unlike existing methodologies that rely on external biological priors, such as protein-protein interaction networks or reference graphs, BioGAT-LGG constructs gene-driven correlation graphs, enabling the model to learn biologically meaningful molecular interactions. To improve feature interpretability and reduce dimensionality, LASSO regression is performed during model training. The model achieved 98.03% accuracy, with precision (98.12%), recall (97.74%), and F1-score (97.87%) in a stratified 10-fold cross-validation. Extensive analysis and enrichment of known cancer-related pathways, including PI3K-Akt signalling, Small Cell Lung Cancer, and Transcriptional Misregulation in Cancer, identified the biomarkers hsa-mir-3936, MTCO1P40, and CCND2, which were subsequently validated. These results indicate that BioGAT-LGG effectively captures biologically validated mechanisms and can enable clinically significant subtype classification and biomarker-guided decision-making. This framework thus lays a scalable foundation for multi-omics integration in oncology, which can be further adopted in other tumour types.