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arXiv · 2604.24371

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Di Wang·Chupei Tang·Junxiao Kong·Jixiu Zhai·Moyu Tang·Tianchi Lu
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Abstract

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene-expression representations on mutation, copy number variation, pathway, and clinical context, and uses dual-level attention to capture both intra-pathway driver signals and inter-pathway clinical relevance. We evaluated PathMoG on 5,650 patients across 10 TCGA cancer types and observed consistent improvements over representative survival baselines. The framework further provides gene-level, pathway-level, and patient-level interpretability, supporting biologically grounded and clinically relevant risk stratification.

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Di Wang, Chupei Tang, Junxiao Kong, Jixiu Zhai, Moyu Tang, Tianchi Lu. 2026-04-27. PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction. https://arxiv.org/abs/2604.24371

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