Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey

TL;DR

This survey reviews how graph neural networks are increasingly used to integrate multi-omics data for cancer research, highlighting recent trends, methods, and future directions in the field.

Contribution

It provides a comprehensive classification and analysis of GNN-based approaches in multi-omics cancer studies, emphasizing emerging techniques and future research directions.

Findings

Growing adoption of hybrid and interpretable GNN models

Increased use of attention mechanisms and contrastive learning

Emerging focus on patient-specific graphs and knowledge priors

Abstract

The task of data integration for multi-omics data has emerged as a powerful strategy to unravel the complex biological underpinnings of cancer. Recent advancements in graph neural networks (GNNs) offer an effective framework to model heterogeneous and structured omics data, enabling precise representation of molecular interactions and regulatory networks. This systematic review explores several recent studies that leverage GNN-based architectures in multi-omics cancer research. We classify the approaches based on their targeted omics layers, graph neural network structures, and biological tasks such as subtype classification, prognosis prediction, and biomarker discovery. The analysis reveals a growing trend toward hybrid and interpretable models, alongside increasing adoption of attention mechanisms and contrastive learning. Furthermore, we highlight the use of patient-specific graphs and knowledge-driven priors as emerging directions. This survey serves as a comprehensive resource for researchers aiming to design effective GNN-based pipelines for integrative cancer analysis, offering insights into current practices, limitations, and potential future directions.