PPI-Net connects molecular protein interactions to functional processes in disease

2026-05-08

AI Summary

This study addresses the challenge of bridging molecular-level protein–protein interactions with higher-order functional processes in disease through a cross-scale, interpretable modeling framework. The authors propose a hierarchical graph neural network that uniquely integrates the STRING protein–protein interaction network with the Reactome pathway hierarchy. Leveraging graph attention mechanisms, the model aggregates patient-specific multi-omics data—RNA-seq and DNA meth日晚间—bottom-up into coherent functional programs. A multi-layer supervised learning strategy enables interpretable integration from gene-level signals to biologically meaningful modules. Evaluated across ten cancer types in TCGA, the approach achieves over 90% accuracy, outperforming PPI-only models by 6.7% and surpassing single-head prediction by 12.3%. The method successfully recapitulates known oncogenic modules such as TP53–AKT and uncovers novel functional programs.

Abstract

Understanding how molecular alterations propagate across biological systems to drive disease remains a central challenge. Although high-throughput profiling enables comprehensive characterization of tumor states, most models neglect structured biological relationships or lack interpretability across scales. Here we present PPI-Net, a hierarchical graph neural network that integrates protein-protein interaction (PPI) networks with pathway-level representations to model disease from molecular interactions to functional processes. Patient-specific molecular profiles are embedded within a shared interaction network from STRING and propagated through a multi-layer Reactome hierarchy using graph attention, enabling aggregation of gene-level signals into higher-order biological programs. Across RNA-seq data from ten cancer types from The Cancer Genome Atlas, PPI-Net achieves robust predictive performance, with balanced accuracy exceeding 90% in multiple cohorts. Comparative analysis on RNA-Seq data from breast cancer demonstrated that PPI-Net's integration of the Reactome hierarchy improved balanced accuracy by 6.7% relative to a PPI-only model, while hierarchical multi-level supervision improved balanced accuracy by 12.3% relative to using only a single top-level prediction head. Applying a multi-omics approach using RNA-seq and methylation data improves model interpretation, recovering canonical oncogenic modules, including TP53-AKT signaling and stress response pathways, while revealing convergence onto coherent programs such as ion signaling and cellular responses to stimuli. These results demonstrate that integrating interaction networks with pathway hierarchies enables accurate prediction while providing mechanistic insight into cancer biology.

Problem

• molecular alterations
• disease propagation
• biological systems
• protein-protein interactions
• pathway hierarchies

Innovation

• hierarchical graph neural network
• protein-protein interaction
• pathway hierarchy
• multi-omics integration
• graph attention