Discovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data

Abstract

Glioblastoma (GBM) is the most common malignant brain cancer in adults. Investigating the regulatory mechanisms underlying GBM is effective for the in-depth study of GBM. The Cancer Genome Atlas (TCGA) project is producing large-scale data and makes the comprehensive study of the diverse regulatory mechanisms underlying GBM possible. Although there have been research studies on GBM with large-scale data, distinguishing different regulatory mechanisms and identifying the key regulation types remain challenging. In this study, we integrated multi-dimensional data of differentially expressed genes in GBM: copy number variation (CNV), gene expression, miRNA expression and methylation, by performing partial correlation analysis with the Lasso technique. Our results showed that there were single-factor and multi-factor regulatory mechanisms in GBM. In further analysis of the regulation subtypes, we discovered that single-factor and multi-factor regulations are potentially distinct in functionality. Moreover, multi-factor regulations especially the key regulation subtypes may be more relevant to GBM and affect many GBM-related genes such as ERBB2 and MAPK1. This study not only verifies the utility of multi-dimensional data integration into GBM research but also distinguishes the key multi-factor regulatory subtypes that may drive pathogenesis of GBM from various regulatory mechanisms.