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      A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism

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          Abstract

          Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC.

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          Gene Ontology: tool for the unification of biology

          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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Ferroptosis: an iron-dependent form of nonapoptotic cell death.

              Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                OMICS
                OMICS
                omi
                OMICS : a Journal of Integrative Biology
                Mary Ann Liebert, Inc. (140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA )
                1536-2310
                1557-8100
                01 July 2018
                01 July 2018
                01 July 2018
                : 22
                : 7
                : 502-513
                Affiliations
                [ 1 ]Center for Quantitative Medicine, UConn Health , Farmington, Connecticut.
                [ 2 ]Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center , Buffalo, New York.
                [ 3 ]Department of Molecular Biology and Biophysics, UConn Health , Farmington, Connecticut.
                [ 4 ]Jackson Laboratory for Genomic Medicine , Farmington, Connecticut.
                Author notes
                [*]Address correspondence to: Anna Konstorum, PhD, Center for Quantitative Medicine UConn Health, Farmington, CT 06030, konstorum@ 123456uchc.edu
                Article
                10.1089/omi.2018.0060
                10.1089/omi.2018.0060
                6059353
                30004845
                6d4d64ec-f218-442b-ae68-a0810a440c03
                © Anna Konstorum, et al., 2018. Published by Mary Ann Liebert, Inc.

                This Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                Page count
                Figures: 3, Tables: 3, References: 77, Pages: 12
                Categories
                Original Research

                ovarian cancer,microarrays,fatty acid metabolism,iron
                ovarian cancer, microarrays, fatty acid metabolism, iron

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