Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Abstract

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Introducing Critical Genes in Response to Photodynamic Therapy: A Network Analysis

“…It has been expressed that the detection of regulatory relationships between genes is a suitable tool to analyze molecular events in the studied systems. 18,19 Regulatory relationships between the introduced hub-bottlenecks are displayed in Figure 4. MYC remains isolated and has no connection with the other genes.…”

Experimental guidance for discovering genetic networks through hypothesis reduction on time series

“…Ideally, these approaches utilize data that describe the entire system and is relatively easy to collect. For Gene Regulatory Networks (GRNs) that control programs of gene expression, transcriptomics measurements have been used to infer underlying network topology [ 8 , 9 ]. For GRNs that control gene expression dynamics, time-series transcriptomic measurements have been used to infer both network topology and the type of interactions (activation or repression) enabling the construction of directed network graphs, e.g.…”