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.
@article{arxiv.2604.24371,
title = {PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction},
author = {Di Wang and Chupei Tang and Junxiao Kong and Jixiu Zhai and Moyu Tang and Tianchi Lu},
journal= {arXiv preprint arXiv:2604.24371},
year = {2026}
}
Comments
9 pages, 5 figures, 3 tables. Source code available at https://github.com/wangzoyou/pathmog