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Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection critical for improving survival rates, yet conventional methods like CT scans often yield high false-positive rates. This study introduces M-GNN, a graph neural network framework leveraging GraphSAGE, to enhance early lung cancer detection through metabolomics. We constructed a heterogeneous graph integrating metabolomics data from 800 plasma samples (586 cases, 214 controls) with demographic features and Human Metabolome Database annotations, employing GraphSAGE and GAT layers for inductive learning on 107 metabolites, pathways, and diseases. M-GNN achieved a test accuracy of 89% and an ROC-AUC of 0.92, with rapid convergence within 400 epochs and robust performance across ten random seeds; key predictors included age, height, choline, Valine, Betaine, and Fumaric Acid, reflecting smoking and metabolic dysregulation. This framework offers a scalable, interpretable tool for precision oncology, surpassing benchmarks by capturing complex biological interactions, though limitations like synthetic data biases and computational demands suggest future validation with real-world cohorts and optimization. M-GNN advances lung cancer screening, promising improved survival through early detection and personalized strategies.
Vaida et al. (Tue,) studied this question.