Gene regulatory network integration with multi-omics data enhances survival predictions in cancer

Abstract

The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. The role of gene regulation as a core driver of biological processes and its role in the development and progression of cancer has been well established. Transcriptional regulation, a crucial aspect of gene regulation, can be represented as a network of interactions between regulators (such as transcription factors) and their target genes. These networks are known as gene regulatory networks (GRNs). Several joint dimensionality reduction (JDR) methods and tools for integrating multi-modal data have been developed in recent years. This study presents a new approach to integrate multi-modal data of different dimensions to consider sample-specific GRNs with multi-omics data in ten cancer datasets from The Cancer Genome Atlas (TCGA). We compare JDR models that include GRNs with those that do not, assessing their association with patient survival. We find that including GRNs can improve associations with patient survival across several cancer types. Focusing on liver cancer, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression.