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Transcriptomic Analysis of the Aquaporin (AQP) Gene Family

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Transcriptomic Analysis of the Aquaporin (AQP) Gene Family Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples. Twenty differentially expressed genes were Biomarker identified, of which nineteen were downregulated and one up-regulated. A molecular panel of four genes AQP7 (Aquaporin 7 e AQP7; Archain 1 e ARCN1; Exocyst Complex Component 3 e EXOC3; Coatomer Protein ARCN1 Complex Subunit Epsilon e COPE) were identified as potential prognostic markers associated with overall survival. Conclusion: These outcomes should be further assessed in vitro in order to fully understand the role of these genes in the pathophysiological mechanism of PDAC. © 2019 IAP and EPC. Published by Elsevier B.V. All rights reserved. Introduction the retroperitoneal location of the pancreas, the early stages of this type of cancer are usually asymptomatic. The disease is generally Pancreatic cancer (PC) is one of the leading cancer-related advanced when it is diagnosed and is associated with poor prog- causes of death worldwide and the fourth cause of cancer mortal- nosis [5]. In fact, the 5-year survival rate of PC is approximately 6% ity in the US [1,2]. More than 85% of the cases presenting with [5]. Depending on the degree of differentiation along with the effect pancreatic cancer are ductal adenocarcinomas (PDAC). In most of tumor microenvironment, the malignancy may show poorly to cases, the tumor is located in the head of the pancreas [3,4]. Due to well-formed glands or infiltrating cells forming sheets [3,4]. In spite of the continuous research efforts, the PC-related mortality rates are increasing and it is projected that in 2030 pancreatic cancer will be the second cancer-related cause of mortality [6]. * Corresponding author. It is well known that the tumor development, invasion, migration E-mail addresses: [email protected] (D.E. Magouliotis), [email protected] (V.S. Tasiopoulou), [email protected] and metastasis depend on the tumor microenvironment and meta- (K. Dimas), [email protected] (N. Sakellaridis), [email protected] bolism [7]. In addition, many studies have shown that water balance (K.A. Svokos), [email protected] (A.A. Svokos), [email protected] and glycerol metabolism play a crucial role in maintaining cell (D. Zacharoulis). https://doi.org/10.1016/j.pan.2019.02.006 1424-3903/© 2019 IAP and EPC. Published by Elsevier B.V. All rights reserved. D.E. Magouliotis et al. / Pancreatology 19 (2019) 436e442 437 function [8,9]. Aquaporins (AQPs) constitute a family of 13 (AQP0-12) GeneMANIA analysis of the significantly differentially expressed transmembrane channel proteins that facilitate the transcellular components of the AQP interactome in pancreatic cancer water movement. In fact, they have been implicated in tumor growth and metastasis, by enhancing cell migration, cell-matrix adhesion, GeneMANIA (http://genemania.org/)[18] is a tool using gene glycerol uptake, and by interacting with oncogenes [8,9]. For ontology algorithms in order to predict the function of a gene or a set instance, AQP1,AQP3, AQP5, and AQP9 have been associated with the of genes to generate a connectivity map. Functional and interaction pathogenesis of colorectal cancer [10,11]. Further evidence has also enrichments were performed during November 2018. The func- highlighted the role of AQP1 in tumor angiogenesis and cell migra- tional annotation enrichment analysis of Gene Ontologies (GO) was tion [7]. In the same context, the expression level of AQP3 was performed with using the GeneMania software mainly due to its increased in gastric cancer compared to normal gastric mucosa attribute to produce both the standard GO enrichment analysis [10,11], thus having been implicated in the epithelial to mesenchymal along with predictions of functional relationships between genes by transition (EMT) and PI3K/AKT/SNAIL signaling pathways [12]. mapping known relationships from other organisms via orthology. Despite the recognized role of the expression of certain mem- The significant enrichments were further assessed by calculating bers of the AQP gene family in normal and cancer conditions their false discovery rates (FDR). The functions of the resulting [13e16], their expression in PDAC has not been fully investigated. proteins of genes within the AQP gene network were extracted from This information prompted us to study the role of AQP gene family GeneCards - The Human Gene Compendium (http://www. members and their interactome in PDAC. The aim of our study was genecards.org/), which is a database of human genes providing to assess the differential expression of AQP0-12 mRNA in PDAC systematic information on all genomic and functional aspects. compared with healthy tissue using transcriptome microarray data of three independent pancreatic adenocarcinoma datasets. Investigation of the potential prognostic value of the significantly Furthermore, we investigated the AQP protein interactors, struc- differentially expressed members of the AQP interactome tured their gene network and performed Kaplan-Meier analysis in order to identify novel candidate genes that in conjunction with To evaluate the potential prognostic significance of the signifi- AQP genes may be used as biomarkers. cantly differentially expressed AQP interactome members, we constructed survival curves using the publicly available survival Materials and methods data of The Cancer Genome Atlas (TCGA). In order to perform the analysis, we used the PROGgeneV2 Prognostic Database (Indiana ConsensusPathDB construction of the AQP interactome University) [19] and we calculated the median value of gene expression to group the patients in either high expressing (above A bioinformatics analysis of the matrix of interactors associated median) or low expressing (below median) each significantly with the AQP gene family was performed in order to identify differentially expressed gene. possible partners of the AQP genes involved in cancer. The AQP gene network was analyzed using the ConsensusPathDB [17] dur- Gene set enrichment analysis (GSEA) of the four prognostic markers ing March 2018 (http://cpdb.molgen.mpg.de/). The Con- sensusPathDB is a database that integrates networks of interactions We performed GSEA using the David Bioinformatics Resources regarding binary and complex protein-protein, genetic, metabolic, 6.7 [20,21] and GeneMania in order to obtain further information signaling, gene regulatory and drug-target interactions. The choice regarding the four prognostic markers mediated biological path- of the ConsensusPathDB platform was based on its integration of 32 ways involved in the pathogenesis of PDAC. different public databases for interactions that have been curated, thus establishing a high level of data extraction quality. Statistical analysis AQP interactome gene expression profile in pancreatic cancer The results were analyzed using GraphPad Prism 8.0 for Mac (GraphPad Software, San Diego, CA). Normal distribution of the We used the PubMed Gene Expression Omnibus (GEO) database data was performed by application of the D'Agostino and Pearson (https://www.ncbi.nlm.nih.gov/gds) to investigate the expression Omnibus normality test. Comparisons of gene expressions were profile of the AQP gene family interactome in patients with performed with two-tailed unpaired t-test for parametric data and pancreatic cancer compared with healthy controls. PubMed GEO is Mann-Whitney U test for nonparametric data. P values were cor- a repository of publicly available curated gene expression datasets, rected for multiple comparisons by the calculation of Q statistic as well as original series and platform records. All microarray data (Benjamini-Hochberg). Differences that
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