Integration of Gene Expression Data and Methylation Reveals Genetic Networks for Glioblastoma

TL;DR

This study introduces Regression2Net, a novel computational method for integrating gene expression and methylation data to identify genetic networks associated with glioblastoma, revealing key pathways and potential therapeutic targets.

Contribution

The paper presents Regression2Net, a new approach for analyzing multi-omics data to uncover genetic networks in glioblastoma, highlighting the link between methylation and tumor pathology.

Findings

Identified 284 and 447 candidate genes linked to glioblastoma.

Revealed over-representation of cancer pathways, especially in methylation networks.

Discovered genes related to energy metabolism, cell cycle, and drug resistance.

Abstract

Motivation: The consistent amount of different types of omics data requires novel methods of analysis and data integration. In this work we describe Regression2Net, a computational approach to analyse gene expression and methylation profiles via regression analysis and network-based techniques. Results: We identified 284 and 447 unique candidate genes potentially associated to the Glioblastoma pathology from two networks inferred from mixed genetic datasets. In-depth biological analysis of these networks reveals genes that are related to energy metabolism, cell cycle control (AATF), immune system response and several types of cancer. Importantly, we observed significant over- representation of cancer related pathways including glioma especially in the methylation network. This confirms the strong link between methylation and glioblastomas. Potential glioma suppressor genes ACCN3 and ACCN4 linked to NBPF1 neuroblastoma breakpoint family have been identified in our expression network. Numerous ABC transporter genes (ABCA1, ABCB1) present in the expression network suggest drug resistance of glioblastoma tumors.