Network Modeling and Analysis of Normal and Cancer Gene Expression Data

Abstract

Network modelling is an important approach to understand cell behaviour. It has proven its effectiveness in understanding biological processes and finding novel biomarkers for severe diseases. In this study, using gene expression data and complex network techniques, we propose a computational framework for inferring relationships between RNA molecules. We focus on gene expression data of kidney renal clear cell carcinoma (KIRC) from the TCGA project, and we build RNA relationship networks for either normal or cancer condition using three different similarity measures (Pearson's correlation, Euclidean distance and inverse Covariance matrix). We analyze the networks individually and in comparison to each other, highlighting their differences. The analysis identified known cancer genes/miRNAs and other RNAs with interesting features in the networks, which may play an important role in kidney renal clear cell carcinoma.