Explainable Multilayer Graph Neural Network for Cancer Gene Prediction

TL;DR

This paper introduces EMGNN, an explainable multilayer graph neural network that leverages multiple gene interaction networks and multi-omics data to improve cancer gene prediction accuracy and interpretability.

Contribution

The paper presents a novel multilayer graph neural network approach that integrates multiple biological networks for more accurate and explainable cancer gene prediction, outperforming existing methods.

Findings

EMGNN achieves a 7.15% higher AUPR than state-of-the-art methods.

EMGNN effectively integrates multiple gene networks to resolve conflicting predictions.

The model provides biological insights through feature importance and gene set enrichment analysis.

Abstract

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanation for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple genegene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve (AUPR) over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research.