用通路模块化图网络，提升多组学癌症生存预测准确率
PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction
- 将基因数据按354个KEGG通路分组，构建模块化图结构
- 在10种癌症、5650名患者上优于主流生存模型
- 支持基因、通路、患者三级可解释性，适合临床风险分层
癌症生存预测面临高维、异质且分散在相互作用的基因与通路中的预后信号挑战。我们提出PathMoG，一种以通路为中心的模块化图神经网络，用于多组学生存预测。PathMoG将全基因组输入重构为354个基于KEGG的通路模块，引入分层组学调控模块，根据突变、拷贝数变异、通路和临床背景调节基因表达表征，并使用双层注意力机制捕捉通路内驱动信号与通路间临床相关性。我们在10种TCGA癌症类型共5,650名患者上评估了PathMoG，结果表明其在生存预测上持续优于代表性基线模型。该框架还提供基因级、通路级和患者级可解释性，支持生物学合理且临床相关的风险分层。
原文摘要 · Abstract (English)
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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