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Pancreatic cancer is one of the deadliest cancers with rapid disease progression. Further elucidation of its underlying molecular mechanisms and novel biomarkers for early detection is necessary. Exosomes are small extracellular vesicles that are released by multiple cell types acting as message carriers during intercellular communication and are promising biomarker candidates. However, the role of pancreatic cancer cell-derived exosomes in cancer progression and the application of these vesicles as novel diagnostic biomarkers have not been fully studied. In this study, we found that PC-1.0 (a highly malignant pancreatic cell line) cell-derived exosomes could be taken up by and enhance PC-1 (a moderately malignant pancreatic cell line) cell proliferation, migration and invasion abilities. We identified ZIP4 as the most upregulated exosomal protein in PC-1.0 cells from our proteomic analysis. In vitro and in vivo (a subcutaneous BALB/c nude mouse model) studies showed that exosomal ZIP4 can significantly promote pancreatic cancer growth. Using clinical blood samples, we compared the diagnostic values of serum exosomal ZIP4 levels between malignant pancreatic cancer patients (n = 24) and benign pancreatic disease patients (n = 32, AUC = .89), and between biliary disease patients (n = 32, AUC = .8112) and healthy controls (n = 46, AUC = .8931). In conclusion, exosomal ZIP4 promotes cancer growth and is a novel diagnostic biomarker for pancreatic cancer.
Pubmed ID: 30007115
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Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.
View all literature mentionsComputable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.
View all literature mentionsA database for phylogenetic classification for proteins encoded in complete genomes. Clusters of Orthologous Groups of proteins (COGs) were delineated by comparing protein sequences encoded in complete genomes, representing major phylogenetic lineages. Each COG consists of individual proteins or groups of paralogs from at least 3 lineages and thus corresponds to an ancient conserved domain. Please be aware that COGs hasn't been updated in many years and will not be.
View all literature mentionsCommercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.
View all literature mentionsSoftware for image processing, analysis, and editing. The software includes features such as touch capabilities, a customizable toolbar, 2D and 3D image merging, and Cloud access and options.
View all literature mentionsA portal that provides visualization, analysis and download of large-scale cancer genomics data sets.
View all literature mentionsHarvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.
View all literature mentionsWeb application and database for analyzing cancer transcriptome data. It also has applications is facilitating tumor subgroup gene expression and survival analyses.
View all literature mentionsWeb server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.
View all literature mentionsMus musculus with name NU/J from IMSR.
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