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Can We Assume the Gene Expression Profile As a Proxy for Signaling Network Activity? Publisher Pubmed



Piran M1 ; Karbalaei R2 ; Piran M1 ; Aldahdooh J4 ; Mirzaie M5 ; Ansaripour N6 ; Tang J4 ; Jafari M4
Authors
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Authors Affiliations
  1. 1. Bioinformatics and Computational Biology Research Center, Shiraz University of Medical Sciences, P.O. Box 71336-54361, Shiraz, Iran
  2. 2. Department of Biology, Temple University, Philadelphia, 19122, PA, United States
  3. 3. Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 14177-55469, Iran
  4. 4. Faculty of Medicine, University of Helsinki, Helsinki, 00270, Finland
  5. 5. Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, P.O. Box 14115-134, Tehran, Iran
  6. 6. Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, OX3 7LF, United Kingdom

Source: Biomolecules Published:2020


Abstract

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.