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      Protein-Protein Interaction Network Analysis for a Biomarker Panel Related to Human Esophageal Adenocarcinoma

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          Abstract

          Background:

          Esophageal adenocarcinoma (EAC) is one of the mostlethal cancers in the world with a very poor prognosis. Identification of molecular diagnostic methods is an important goal. Since protein-protein interaction (PPI) network analysis is a suitable method for molecular assessment, in the present research a PPI network related to EAC was targeted.

          Material and Method:

          Cytoscape software and its applications including STRING DB, Cluster ONE and ClueGO were applied to analyze the PPI network.

          Result:

          Among 182 EAC-related proteins which were identified, 129 were included in a main connected component. Proteins based on centrality analysis of characteristics such as degree, betweenness, closeness and stress were screened and key nodes were introduced. Two clusters were determined of which only one was significant statistically. Gene ontology revealed 50 terms in three groups associated with EAC.

          Conclusion:

          The findings indicate nine crucial proteins could form a candidate biomarker panel for EAC. Furthermore, an important cluster with 27 proteins related to the disease was identified. Gene ontology analysis of this cluster showed main related terms to closely correspond with those for colorectal cancer.

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          Most cited references38

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            Damian Szklarczyk, John Morris, Helen Cook … (2016)
            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              Detecting overlapping protein complexes in protein-protein interaction networks.

              Tamás Nepusz, Haiyuan Yu, Alberto Paccanaro (2012)
              We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Asian Pac J Cancer Prev
                Journal ID (iso-abbrev): Asian Pac. J. Cancer Prev
                Title: Asian Pacific Journal of Cancer Prevention : APJCP
                Publisher: West Asia Organization for Cancer Prevention (Iran )
                ISSN (Print): 1513-7368
                ISSN (Electronic): 2476-762X
                Publication date (Print): 2017
                Volume: 18
                Issue: 12
                Pages: 3357-3363
                Affiliations
                [1 ] Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
                [2 ] Proteomics Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
                [3 ] Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
                Author notes
                [* ] For Correspondence: Tavirany@ 123456yahoo.com
                Article
                Publisher ID: APJCP-18-3357
                DOI: 10.22034/APJCP.2017.18.12.3357
                PMC ID: 5980895
                PubMed ID: 29286604
                SO-VID: ca8223e5-e5ba-415f-9fd0-74872765452b
                Copyright © Copyright: © Asian Pacific Journal of Cancer Prevention
                License:

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

                History
                Categories
                Subject: Research Article

                Keywords: esophageal adenocarcinoma (eac),protein-protein interaction (ppi) network analysis,cluster analysis
                Data availability:
                Keywords: esophageal adenocarcinoma (eac), protein-protein interaction (ppi) network analysis, cluster analysis

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