Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors

SIMPLE SUMMARY: Neoplastic processes are accompanied by the reprogramming of cell metabolism and alteration of the balance between endogenous bioregulators with signaling functions. Prostanoid signaling is a part of the global arachidonic acid pathway and is associated with cancer progression. It includes prostanoids (prostacyclin, thromboxane, and prostaglandins E(2), F(2α), D(2), H(2)), prostanoid metabolizing enzymes, and receptors. We focused on a comparative systematic analysis of expression patterns of target genes, encoding prostanoid enzymes and receptors. We also addressed the possible ways of their regulation at different levels in normal and tumor tissues (expression of genes and proteins, mutation and copy number landscape, promoter methylation status, prediction of tissue-specific master regulators, microRNAs). The results of the systematic analysis made it possible to suggest models of regulation of differentially expressed prostanoid enzymes and receptors. The associations between gene expression signatures and patients’ overall survival rates were established which can be valuable for translational biomedicine. ABSTRACT: Cancer-associated disturbance of prostanoid signaling provides an aberrant accumulation of prostanoids. This signaling consists of 19 target genes, encoding metabolic enzymes and G-protein-coupled receptors, and prostanoids (prostacyclin, thromboxane, and prostaglandins E(2), F(2α), D(2), H(2)). The study addresses the systems biology analysis of target genes in 24 solid tumors using a data mining pipeline. We analyzed differential expression patterns of genes and proteins, promoter methylation status as well as tissue-specific master regulators and microRNAs. Tumor types were clustered into several groups according to gene expression patterns. Target genes were characterized as low mutated in tumors, with the exception of melanoma. We found at least six ubiquitin ligases and eight protein kinases that post-translationally modified the most connected proteins PTGES3 and PTGIS. Models of regulation of PTGIS and PTGIR gene expression in lung and uterine cancers were suggested. For the first time, we found associations between the patient’s overall survival rates with nine multigene transcriptomics signatures in eight tumors. Expression patterns of each of the six target genes have predictive value with respect to cytostatic therapy response. One of the consequences of the study is an assumption of prostanoid-dependent (or independent) tumor phenotypes. Thus, pharmacologic targeting the prostanoid signaling could be a probable additional anticancer strategy.