R of lung metastases. Summary/conclusion: CLIC4 levels in EVs from biological fluids may have worth as a cancer biomarker, in conjunction with other markers, to detect or analyse tumour progression or recurrence.PT05.Bioinformatics analysis of metabolites present in urinary exosomes identify metabolic pathways altered in prostate cancer Marc Clos-Garcia1; Pilar Sanchez-Mosquera2; Patricia Zu ga-Garc 2; Ana R. Cortazar2; Ver ica Torrano2; Ana Loizaga-Iriarte3; Aitziber UgaldeOlano3; Isabel Lacasa4; F ix Royo5; Miguel Unda3; Arkaitz Carracedo2; Juan M. Falc -P ez5 Exosomes Laboratory, CIC bioGUNE, Derio, Spain; 2CIC bioGUNE, Derio, Spain; 3RSV G proteins site Basurto University Hospital, Bilbao, Spain; 4Hospital Basurto, Bilbao, Spain; 5CIC bioGUNE, CIBERehd, Bizkaia Science and Technology Park, Derio, Bizkaia, Spain, Derio, SpainPT05.Chloride intracellular channel protein 4 (CLIC4) is really a serological cancer biomarker released from tumour epithelial cells through extracellular vesicles and expected for metastasis Vanesa C. Sanchez1; Alayna Craig-Lucas1; Bih-Rong Wei2; Abigail Read2; Mark Simpson2; Ji Luo1; Kent Hunter2; Stuart YuspaNational Institutes of Health (NIH), Bethesda, USA; 2LCBG NCI NIH, Bethesda, USABackground: CLIC4 can be a hugely conserved metamorphic protein initially described as an ion channel. It translocates to the nucleus serving as an integral component of TGF- signalling. In numerous cancers, CLIC4 is a tumour suppressor, excluded in the nucleus and lost in the cytoplasm of progressing cancer cells. In contrast, CLIC4 is upregulated inside the tumour stroma acting as a tumour promoter. CLIC4 lacks aBackground: Metabolomics is an omics discipline with high potential to determine new biomarkers, but it is limited to metabolites, lacking of details on the context and/or integration into metabolic pathways. Previously, using metabolomics data obtained from urine EVs, we identified altered metabolites amongst prostate cancer (PCa) sufferers and benign hyperplasia (BPH) patients. Within the existing work, we created a bioinformatics workflow to identify gene-encoding proteins involved inside the metabolism of those metabolites and to map them into metabolic pathways. Employing publicly available, gene expression for prostate cancer datasets, we identified a number of genes which regulation was altered, in agreement with the alterations observed at the metabolite level. Methods: R scripts had been developed for retrieving details from KEGG and HMDB database, particularly, enzymes and genes associated with the metabolites of interest. Combining each genes and metabolites lists, the script searched for metabolic pathway that could be altered. Lastly, gene expression information was analysed in offered databases for all those genes of interest. Results: We detected 76 metabolites that were substantially unique in between prostate cancer and benign prostate hyperplasia. We identified 149 enzymes involved within the metabolism of these metabolites. From them, the levels of their encoding genes have been evaluated inside the PCa gene expression data sets. As a result, the levels of 7 gene-encoding enzymes were found altered in PCa and had been in concordance with the metabolite levels observed in urinary EVs. Our results indicate that steroid hormones, leukotriene and prostaglandin, linoleate, glycerophospholipid and tryptophan metabolisms and urea and TCA cycles, are altered in PCa.ISEV 2018 abstract bookSummary/conclusion: In this Carbonic Anhydrase 5A (CA5A) Proteins site perform, we demonstrated that bioinformatics tools applied for combinin.