Network-based integration of multi-omics data for prioritizing cancer genes

TLDR

Cancer involves heterogeneous genomic aberrations, promoter hypermethylation, and microRNA expression changes, but how these events shape the transcriptome and proteome remains poorly understood. The study introduces NetICS, a graph‑diffusion method that integrates multi‑omics data on a directed functional interaction network to prioritize cancer genes. NetICS ranks genes by their mediator effect—their network proximity to upstream aberrations and downstream differentially expressed genes and proteins—and aggregates sample‑specific rankings with a robust rank‑aggregation technique. NetICS effectively explains aberration heterogeneity by converging to common differentially expressed genes and proteins, and it outperforms existing methods in predicting known cancer genes and generating robust gene lists across five TCGA cancer types. NetICS is available at https://github.com/cbg-ethz/netics, with supplementary data hosted on Bioinformatics online.

Abstract

Abstract Motivation Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information Supplementary data are available at Bioinformatics online.