TY - JOUR T1 - Can we assume the gene expression profile as a proxy for signaling network activity? JF - bioRxiv DO - 10.1101/643866 SP - 643866 AU - Mehran Piran AU - Reza Karbalaee AU - Mehrdad Piran AU - Jehad Aldahdooh AU - Mehdi Mirzaie AU - Naser Ansari-Pour AU - Jing Tang AU - Mohieddin Jafari Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/14/643866.abstract N2 - Studying relationships among gene-products by gene 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, i.e., miRNA and proteins, and assumed correlation of transcript and protein expression levels. All these efforts partook in the current understanding of signaling network models and expanded the signaling databases, which include interactions of the gene-products extracted based on either the literature or direct and indirect experiments. In fact, due to unavailability or high-cost of the experiments, most of the studies do not usually look for direct gene-protein or protein-protein interactions, and some parts of these networks are contradictory. Besides, it is now a standard practice step to accomplish enrichment analysis on biological annotations, especially in omics research, to make claims about the potentially implicated biological pathways in any perturbation. Specifically, upon identifying differentially expressed genes (DEGs), they are spontaneously presumed as dysregulated genes. Then, molecular mechanistic insights are proposed for disease etiology and drug discovery based on statistically enriched biological processes. In this study, using four common and comprehensive databases i.e., GEO, GDSC, KEGG, and OmniPath, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the rate of coherency or sign consistency between the expression level and the causal relationships among the gene pairs. We illustrated that the signaling network was not more consistent or coherent with the recorded expression profile compared to the random relationships. Finally, we provided the pieces of evidence and concluded that gene-product expression data, especially at the transcript level, are not reliable or at least insufficient to infer biological causal relationships among genes and in turn describe cellular behavior. ER -