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On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

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

Eman Mohammed Hamid¹, Murtada K Elbashir², Nosiba Yousif Ahmed¹

¹Department of Computer Science, Faculty of Mathematical and Computer Science, University of Gezira, Wad Madani, Sudan.

Discover Oncology

|January 16, 2026

Summary

BioGAT-LGG, a deep learning model, accurately classifies Lower-Grade Glioma (LGG) subtypes using multi-omics data. It identifies novel biomarkers for personalized cancer therapies and decision-making.

Keywords:

Biomarker identification Cancer subtype classification Correlation-based graph GATv2 Gene ontology KEGG pathways LGG Multi-omics data

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Accurate cancer subtype classification is essential for developing personalized therapies.
Existing methods often rely on external biological data, limiting their scope.
Integrating multi-omics data offers a comprehensive approach to understanding cancer complexity.

Purpose of the Study:

To develop a deep learning framework, BioGAT-LGG, for Lower-Grade Glioma (LGG) subtype classification.
To discover novel biomarkers by integrating multi-omics data (mRNA, miRNA, DNA methylation).
To establish a gene-driven correlation graph approach for learning molecular interactions.

Main Methods:

Utilized a correlation-based Graph Attention Network version 2 (GATv2) for multi-omics integration.
Employed LASSO regression for feature interpretability and dimensionality reduction.
Performed stratified 10-fold cross-validation for robust performance evaluation.

Main Results:

Achieved high accuracy (98.03%) with excellent precision (98.12%), recall (97.74%), and F1-score (97.87%).
Identified and validated key biomarkers: hsa-mir-3936, MTCO1P40, and CCND2.
Pathway enrichment analysis confirmed relevance to cancer-related signaling.

Conclusions:

BioGAT-LGG effectively classifies LGG subtypes and discovers clinically relevant biomarkers.
The framework captures biologically validated mechanisms for informed decision-making.
This approach provides a scalable foundation for multi-omics integration in oncology.