Gene targeting in disease networks

TL;DR

This paper discusses gene targeting scores derived from regulatory networks, demonstrating their effectiveness in identifying disease-relevant processes in complex phenotypes like pancreatic cancer, beyond traditional differential expression analysis.

Contribution

It introduces gene targeting scores as a novel approach for analyzing regulatory networks and showcases their utility in disease phenotype characterization.

Findings

Gene targeting scores reveal differential processes missed by expression analysis.

Application to pancreatic cancer highlights their practical utility.

Gene targeting scores enhance understanding of complex disease mechanisms.

Abstract

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis or pancreatic ductal adenocarcinoma demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes; processes which would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.