DOCSLIB.ORG

Unravelling the Genetic and Pathophysiological Complexity of the Mitochondrial Myopathies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Unravelling the Genetic and Pathophysiological Complexity of the Mitochondrial Myopathies Unravelling the genetic and pathophysiological complexity of the mitochondrial myopathies Author: R.G.J. Dohmen Final Graduation Report Unravelling the genetic and pathophysiological complexity of the mitochondrial myopathies University Maastricht General data: Graduation subject Author Mitochondrial Myopathies Richard Gerardus Johannes Dohmen [email protected] Graduation term 23-01-2012 to 25-06-2012 Student number 2016701 Version Deadline 1 May/ June 2011 Education contact information Internship contact information School of Life Sciences and Environment University Maastricht Technology Clinical Genomics Department Lovensdijkstraat 61-63 Universiteitssingel 50 4818 AJ Breda Phone: 076 525 05 00 6229 ER Maastricht Phone: 043 388 19 95 Supervisor ATGM Internship mentor Julian Ramakers Prof. Dr. Bert Smeets [email protected] [email protected] Supervisor UM Ing. Rick Kamps [email protected] Page ii Preface The performed graduation term, 23rd of January 2012 to 25th of June 2012, is documented in this final report. During the graduation my knowledge of the mechanisms of genomics, the use of different databases and my practical skills were improved. The obtained knowledge and results during the graduation are included in this report. I would like to thank Bert Smeets for the opportunity to become an intern at Clinical Genomics. Second of all I want to thank Mike Gerards, Iris Boesten, Auke Otten and Bianca van den Bosch for their assistance, knowledge and practical tricks which they shared with me. Further more I thank the rest of the department Clinical Genomics for a wonderful and educational 9 and half months. And last but definitely not least my supervisor, Rick Kamps. I thank him for all the knowledge and the support and guidance he gave me, his structured and calm way of handling things really inspired me. Page iii Table of contents Preface ................................................................................................................................................................................................................iii Summary.........................................................................................................................................................................................................vii Samenvatting...........................................................................................................................................................................................viii 1. Introduction..............................................................................................................................................................................................1 2. Theoretical background ..........................................................................................................................................................2 2.1 Whole Exome – Enrichment .................................................................................................2 2.2 Illumina Sequencing ..............................................................................................................3 2.3 Bioinformatics........................................................................................................................5 2.3.1 Variant analyses...............................................................................................................5 2.4 Functional tests......................................................................................................................7 2.4.1 Targeting of the protein ..................................................................................................7 2.4.2 Analyses other patients...................................................................................................8 2.4.3 Gene expression levels....................................................................................................8 2.5 Affected patients.....................................................................................................................8 2.5.1 Family DNA 07-2283 ......................................................................................................9 2.5.1.1 LAMA3 ........................................................................................................................9 2.5.1.2 SYNPO2 ......................................................................................................................9 2.5.1.3 DCHS2 ........................................................................................................................9 2.5.1.4 SLC6A8.......................................................................................................................9 2.5.1.5 Zyxin .........................................................................................................................10 2.5.1.6 KIAA1109..................................................................................................................10 2.5.1.7 Transformation fibroblast - Myogenesis ................................................................10 2.5.2 Family DNA 08-5759....................................................................................................10 2.5.2.1 KRTAP10-6...............................................................................................................11 2.5.2.2 PSPH ........................................................................................................................11 2.6 Aim/ hypothesis...................................................................................................................11 Page iv 3. Methodology........................................................................................................................................................................................ 12 3.1 Whole Exome Enrichment & Illumina Genoma Analyzer HiSeq 2000 ...........................12 3.1.1 Sample preparation........................................................................................................12 3.1.2 Hybridization .................................................................................................................12 3.1.3 Addition of Index Tags by Post-Hybridization Amplification....................................13 3.1.4 Cluster generation .........................................................................................................13 3.1.5 Sequencing by synthesis................................................................................................13 3.2 Data analyses........................................................................................................................13 3.2.1 Validation & segregation...............................................................................................13 3.2.2 Relate function gene to phenotype ..............................................................................14 3.3 Functional tests....................................................................................................................14 3.3.1 Targeting ........................................................................................................................14 3.3.2 Analyses other patients.................................................................................................14 3.3.3 Gene expression levels..................................................................................................15 3.3.4 Transformation fibroblasts ..........................................................................................15 4. Results & Discussion/ Conclusion............................................................................................................................ 16 4.1 Family DNA 07-2283 ...........................................................................................................16 4.1.1 Variants...........................................................................................................................16 4.1.2 Sanger sequencing validation & segregation ...............................................................17 4.1.3 SYNPO2 protein targeting ............................................................................................21 4.1.4 Myogenesis – qPCR gene expression levels ................................................................22 4.2 Family DNA 08-5759...........................................................................................................28 4.2.1 Variants (2) ....................................................................................................................28 4.2.2 Sanger sequencing validation & segregation (2).........................................................29 5. Discussion/ Conclusion & Recommendations..........................................................................................32 References....................................................................................................................................................................................................35 Page v Appendix’.......................................................................................................................................................................................................38 Appendix 1: Flow diagram WE-enrichment.............................................................................39 Appendix 2: pEGFP-n1 ..............................................................................................................40
Load more

Recommended publications
  • Phenotypic Modulation of Smooth Muscle Cells in Atherosclerosis Is Associated with Downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM
    Markers of smooth muscle cells Perisic et al. Phenotypic modulation of smooth muscle cells in atherosclerosis is associated with downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM Perisic L1, Rykaczewska U1, Razuvaev A1, Sabater-Lleal M2, Lengquist M1, Miller CL3, Ericsson I1, Röhl S1, Kronqvist M1, Aldi S1, Magné J2, Vesterlund M4, Li Y2, Yin H2, Gonzalez Diez M2, Roy J1, Baldassarre D5,6, Veglia F6, Humphries SE7, de Faire U8,9, Tremoli E5,6, on behalf of the IMPROVE study group, Odeberg J10, Vukojević V11, Lehtiö J4, Maegdefessel L2, Ehrenborg E2, Paulsson-Berne G2, Hansson GK2, Lindeman JHN12, Eriksson P2, Quertermous T3, Hamsten A2, Hedin U1 1Department of Molecular Medicine and Surgery, Karolinska Institutet, Sweden, 2Department of Medicine, Karolinska Institutet, Sweden, 3Division of Vascular Surgery, Stanford University, USA, 4Science for Life Laboratory, Sweden, 5Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy, 6Centro Cardiologico Monzino, IRCCS, Milan, Italy, 7British Heart Foundation Laboratories, University College of London, Department of Medicine, Rayne Building, London, United Kingdom, 8Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 9Department of Cardiology, Karolinska University Hospital Solna, Karolinska Institutet, Stockholm, Sweden, 10Science for Life Laboratory, Department of Proteomics, Sweden, 11Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Sweden, 12Department of Vascular
    [Show full text]
  • The Transcriptomic Landscape of Prostate Cancer Development and Progression: an Integrative Analysis
    cancers Article The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis Jacek Marzec 1,† , Helen Ross-Adams 1,*,† , Stefano Pirrò 1 , Jun Wang 1 , Yanan Zhu 2, Xueying Mao 2, Emanuela Gadaleta 1 , Amar S. Ahmad 3, Bernard V. North 3, Solène-Florence Kammerer-Jacquet 2, Elzbieta Stankiewicz 2, Sakunthala C. Kudahetti 2, Luis Beltran 4, Guoping Ren 5, Daniel M. Berney 2,4, Yong-Jie Lu 2 and Claude Chelala 1,6,* 1 Bioinformatics Unit, Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (J.M.); [email protected] (S.P.); [email protected] (J.W.); [email protected] (E.G.) 2 Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (Y.Z.); [email protected] (X.M.); solenefl[email protected] (S.-F.K.-J.); [email protected] (E.S.); [email protected] (S.C.K.); [email protected] (D.M.B.); [email protected] (Y.-J.L.) 3 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (A.S.A.); [email protected] (B.V.N.) 4 Department of Pathology, Barts Health NHS, London E1 F1R, UK; [email protected] 5 Department of Pathology, The First Affiliated Hospital, Zhejiang University Medical College, Hangzhou 310058, China; [email protected] 6 Centre for Computational Biology, Life Sciences Initiative, Queen Mary University London, London EC1M 6BQ, UK * Correspondence: [email protected] (H.R.-A.); [email protected] (C.C.) † These authors contributed equally to this work.
    [Show full text]
  • Downloaded 18 July 2014 with a 1% False Discovery Rate (FDR)
    UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer Permalink https://escholarship.org/uc/item/0t47b9ws Author Spiciarich, David Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Chemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Professor Matthew B. Francis Professor Amy E. Herr Fall 2017 Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer © 2017 by David Spiciarich Abstract Chemical glycoproteomics for identification and discovery of glycoprotein alterations in human cancer by David Spiciarich Doctor of Philosophy in Chemistry University of California, Berkeley Professor Carolyn R. Bertozzi, Co-Chair Professor David E. Wemmer, Co-Chair Changes in glycosylation have long been appreciated to be part of the cancer phenotype; sialylated glycans are found at elevated levels on many types of cancer and have been implicated in disease progression. However, the specific glycoproteins that contribute to cell surface sialylation are not well characterized, specifically in bona fide human cancer. Metabolic and bioorthogonal labeling methods have previously enabled enrichment and identification of sialoglycoproteins from cultured cells and model organisms. The goal of this work was to develop technologies that can be used for detecting changes in glycoproteins in clinical models of human cancer.
    [Show full text]
  • Lncrna SNHG8 Is Identified As a Key Regulator of Acute Myocardial
    Zhuo et al. Lipids in Health and Disease (2019) 18:201 https://doi.org/10.1186/s12944-019-1142-0 RESEARCH Open Access LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis Liu-An Zhuo, Yi-Tao Wen, Yong Wang, Zhi-Fang Liang, Gang Wu, Mei-Dan Nong and Liu Miao* Abstract Background: Long noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood. Methods: We performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve. Results: An AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (P SNHG8-ICAM1 = 0.002; P SNHG8-SOCS3 = 0.031).
    [Show full text]
  • Phenotypic Modulation of Smooth Muscle Cells in Atherosclerosis Is Associated with Downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM
    Markers of smooth muscle cells Perisic et al. Phenotypic modulation of smooth muscle cells in atherosclerosis is associated with downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM Matic Perisic L1, Rykaczewska U1, Razuvaev A1, Sabater-Lleal M2, Lengquist M1, Miller CL3, Ericsson I1, Röhl S1, Kronqvist M1, Aldi S1, Magné J2, Paloschi V2, Vesterlund M4, Li Y2, Jin H2, Gonzalez Diez M2, Roy J1, Baldassarre D5,6, Veglia F6, Humphries SE7, de Faire U8,9, Tremoli E5,6, on behalf of the IMPROVE study group, Odeberg J10, Vukojević V11, Lehtiö J4, Maegdefessel L2, Ehrenborg E2, Paulsson- Berne G2, Hansson GK2, Lindeman JHN12, Eriksson P2, Quertermous T3, Hamsten A2, Hedin U1 1Department of Molecular Medicine and Surgery, Karolinska Institutet, Sweden, 2Department of Medicine, Karolinska Institutet, Sweden, 3Division of Vascular Surgery, Stanford University, USA, 4Science for Life Laboratory, Sweden, 5Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy, 6Centro Cardiologico Monzino, IRCCS, Milan, Italy, 7British Heart Foundation Laboratories, University College of London, Department of Medicine, Rayne Building, London, United Kingdom, 8Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 9Department of Cardiology, Karolinska University Hospital Solna, Karolinska Institutet, Stockholm, Sweden, 10Science for Life Laboratory, Department of Proteomics, Sweden, 11Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Sweden,
    [Show full text]
  • Dsir2 and Dmp53 Interact to Mediate Aspects of CR-Dependent Life Span
    β-catenin-mediated hair growth induction effect of 3,4,5-tri-O-caffeoylquinic acid 著者（英） Meriem Bejaoui, Myra Orlina VILLAREAL, Hiroko ISODA journal or Aging publication title volume 11 number 12 page range 4216-4237 year 2019-06 権利 Bejaoui et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. URL http://hdl.handle.net/2241/00157702 doi: 10.18632/aging.102048 Creative Commons : 表示 http://creativecommons.org/licenses/by/3.0/deed.ja www.aging-us.com AGING 2019, Vol. 11, No. 12 Research Paper β-catenin-mediated hair growth induction effect of 3,4,5-tri-O- caffeoylquinic acid Meriem Bejaoui1, Myra O. Villareal1,2,3, Hiroko Isoda1,2,3 1School of Integrative and Global Majors (SIGMA), University of Tsukuba, Tsukuba City, 305-8572 Japan 2Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba City, 305-8572 Japan 3Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba City, 305- 8572 Japan Correspondence to: Hiroko Isoda; email: [email protected] Keywords: 3,4,5-tri-O-caffeoylquinic acid (TCQA), β-catenin, dermal papilla, anagen, Wnt/β-catenin pathway Received: April 23, 2018 Accepted: June 17, 2019 Published: June 29, 2019 Copyright: Bejaoui et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    [Show full text]
  • Views for Entrez
    BASIC RESEARCH www.jasn.org Phosphoproteomic Analysis Reveals Regulatory Mechanisms at the Kidney Filtration Barrier †‡ †| Markus M. Rinschen,* Xiongwu Wu,§ Tim König, Trairak Pisitkun,¶ Henning Hagmann,* † † † Caroline Pahmeyer,* Tobias Lamkemeyer, Priyanka Kohli,* Nicole Schnell, †‡ †† ‡‡ Bernhard Schermer,* Stuart Dryer,** Bernard R. Brooks,§ Pedro Beltrao, †‡ Marcus Krueger,§§ Paul T. Brinkkoetter,* and Thomas Benzing* *Department of Internal Medicine II, Center for Molecular Medicine, †Cologne Excellence Cluster on Cellular Stress | Responses in Aging-Associated Diseases, ‡Systems Biology of Ageing Cologne, Institute for Genetics, University of Cologne, Cologne, Germany; §Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; ¶Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; **Department of Biology and Biochemistry, University of Houston, Houston, Texas; ††Division of Nephrology, Baylor College of Medicine, Houston, Texas; ‡‡European Molecular Biology Laboratory–European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom; and §§Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany ABSTRACT Diseases of the kidney filtration barrier are a leading cause of ESRD. Most disorders affect the podocytes, polarized cells with a limited capacity for self-renewal that require tightly controlled signaling to maintain their integrity, viability, and function. Here, we provide an atlas of in vivo phosphorylated, glomerulus- expressed
    [Show full text]
  • Fibroblasts from the Human Skin Dermo-Hypodermal Junction Are
    cells Article Fibroblasts from the Human Skin Dermo-Hypodermal Junction are Distinct from Dermal Papillary and Reticular Fibroblasts and from Mesenchymal Stem Cells and Exhibit a Specific Molecular Profile Related to Extracellular Matrix Organization and Modeling Valérie Haydont 1,*, Véronique Neiveyans 1, Philippe Perez 1, Élodie Busson 2, 2 1, 3,4,5,6, , Jean-Jacques Lataillade , Daniel Asselineau y and Nicolas O. Fortunel y * 1 Advanced Research, L’Oréal Research and Innovation, 93600 Aulnay-sous-Bois, France; [email protected] (V.N.); [email protected] (P.P.); [email protected] (D.A.) 2 Department of Medical and Surgical Assistance to the Armed Forces, French Forces Biomedical Research Institute (IRBA), 91223 CEDEX Brétigny sur Orge, France; [email protected] (É.B.); [email protected] (J.-J.L.) 3 Laboratoire de Génomique et Radiobiologie de la Kératinopoïèse, Institut de Biologie François Jacob, CEA/DRF/IRCM, 91000 Evry, France 4 INSERM U967, 92260 Fontenay-aux-Roses, France 5 Université Paris-Diderot, 75013 Paris 7, France 6 Université Paris-Saclay, 78140 Paris 11, France * Correspondence: [email protected] (V.H.); [email protected] (N.O.F.); Tel.: +33-1-48-68-96-00 (V.H.); +33-1-60-87-34-92 or +33-1-60-87-34-98 (N.O.F.) These authors contributed equally to the work. y Received: 15 December 2019; Accepted: 24 January 2020; Published: 5 February 2020 Abstract: Human skin dermis contains fibroblast subpopulations in which characterization is crucial due to their roles in extracellular matrix (ECM) biology.
    [Show full text]
  • Quantitative Lymphatic Vessel Trait Analysis Suggests Vcam1 As Candidate Modifier Gene of Inflammatory Bowel Disease
    Genes and Immunity (2010) 11, 219–231 & 2010 Macmillan Publishers Limited All rights reserved 1466-4879/10 $32.00 www.nature.com/gene ORIGINAL ARTICLE Quantitative lymphatic vessel trait analysis suggests Vcam1 as candidate modifier gene of inflammatory bowel disease G Jurisic1, JP Sundberg2, A Bleich3, EH Leiter2, KW Broman4, G Buechler3, L Alley2, D Vestweber5 and M Detmar1 1Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology, ETH Zurich, Zurich, Switzerland; 2The Jackson Laboratory, Bar Harbor, ME, USA; 3Institute for Laboratory Animal Science and Central Animal Facility, Hannover Medical School, Hannover, Germany; 4Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA and 5Max Planck Institute of Molecular Biomedicine, Mu¨nster, Germany Inflammatory bowel disease (IBD) is a chronic debilitating disease resulting from a complex interaction of multiple genetic factors with the environment. To identify modifier genes of IBD, we used an F2 intercross of IBD-resistant C57BL/6J-Il10À/À mice and IBD-susceptible C3H/HeJBir-Il10À/À (C3Bir-Il10À/À) mice. We found a prominent involvement of lymphatic vessels in IBD and applied a scoring system to quantify lymphatic vascular changes. Quantitative trait locus (QTL) analyses revealed a large-effect QTL on chromosome 3, mapping to an interval of 43.6 Mbp. This candidate interval was narrowed by fine mapping to 22 Mbp, and candidate genes were analyzed by a systems genetics approach that included quantitative gene expression profiling, search for functional polymorphisms, and haplotype block analysis. We identified vascular adhesion molecule 1 (Vcam1) as a candidate modifier gene in the interleukin 10-deficient mouse model of IBD.
    [Show full text]
  • Mousenet V2: a Database of Gene Networks for Studying the Laboratory
    Nucleic Acids Research Advance Access published November 2, 2015 Nucleic Acids Research, 2015 1 doi: 10.1093/nar/gkv1155 MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates Eiru Kim1, Sohyun Hwang1,2, Hyojin Kim1, Hongseok Shim1, Byunghee Kang1, Sunmo Yang1, Jae Ho Shim1, Seung Yeon Shin1, Edward M. Marcotte2 and Insuk Lee1,* 1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea and 2Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, TX 78712, USA Downloaded from Received September 08, 2015; Revised October 05, 2015; Accepted October 19, 2015 ABSTRACT INTRODUCTION Laboratory mouse, Mus musculus, is one of the most Geneticists have achieved impressive progress in discover- http://nar.oxfordjournals.org/ important animal tools in biomedical research. Func- ing disease-associated genes and genotypes directly in hu- tional characterization of the mouse genes, hence, mans, but the functional validation and mechanistic follow- has been a long-standing goal in mammalian and hu- up studies of these genes typically relies heavily on the use man genetics. Although large-scale knockout pheno- of laboratory animals. The laboratory mouse (Mus muscu- lus) is the experimental tool of choice for many biomedi- typing is under progress by international collabora- cal researchers, as for example in immunology, cancer bi- tive efforts, a large portion of mouse genome is still ology, and stem cell biology, and there are many ongoing poorly characterized for cellular functions and asso- efforts to characterize mouse biology. In spite of these ex- at University of Texas Austin on November 6, 2015 ciations with disease phenotypes.
    [Show full text]
  • Integrative Analysis of Four Mirna Prognostic Signatures of Prostate Cancer
    Integrative Analysis of four miRNA prognostic signatures of prostate cancer Chen Chi Wenzhou Medical University First Aliated Hospital Xianwu Chen Wenzhou Medical University First Aliated Hospital Liping Yao Wenzhou Medical University First Aliated Hospital Min Li Wenzhou Medical University First Aliated Hospital Lanting Xiang Wenzhou Medical University First Aliated Hospital Xidan Dong Wenzhou Medical University First Aliated Hospital Xiaoling Guo Wenzhou Medical University Second Aliated Hospital Quanbo Zhang ( [email protected] ) Wenzhou Medical University First Aliated Hospital https://orcid.org/0000-0003-3337-3842 Research article Keywords: prostate cancer, bioinformatics, prognosis, microRNA Posted Date: June 24th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-34448/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/17 Abstract Background Prostate cancer (PCa) is the most common urological cancer among men, having a poor prognosis, which is hard to accurately evaluate based on the present methods. MicroRNAs (miRNAs), a class of internal non-coding small RNA, can involve in the regulation of tumor biological function. So far, many researchers have tried to explore the relationship of malignant progress of PCa with miRNA, while there are just limited studies conducting the comprehensive analysis of miRNA in PCa clinical signicance. Methods The data of miRNA and mRNA expressions in PCa were downloaded from TCGA database, and were performed the overall survival (OS) analysis using Survival package of R software to harvest the differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs). The bioinformatics tools such as TargetScan, miRDB, and miRanda were also conducted to forecast the desired target genes related with prognostic DEMs.
    [Show full text]
  • Genome-Wide Expression Profiling of Urinary Bladder Implicates Desmosomal and Cytoskeletal Dysregulation in the Bladder Exstrophy-Epispadias Complex
    755-765.qxd 7/4/2011 01:12 ÌÌ ™ÂÏ›‰·755 INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE 27: 755-765, 2011 755 Genome-wide expression profiling of urinary bladder implicates desmosomal and cytoskeletal dysregulation in the bladder exstrophy-epispadias complex LIHONG QI1,2, KUN CHEN3, DAVID J. HUR4, GARIMA YAGNIK5, YEGAPPAN LAKSHMANAN6, LORI E. KOTCH4, GERALD H. ASHRAFI4, FRANCISCO MARTINEZ-MURILLO7, JEANNE KOWALSKI8, CYRILL NAYDENOV9, LARS WITTLER10,11, JOHN P. GEARHART6, MARKUS DRAAKEN12,13, HEIKO REUTTER12,14, MICHAEL LUDWIG15 and SIMEON A. BOYADJIEV5,6 1Rowe Program in Human Genetics, Departments of 2Public Health Sciences and 3Statistics, University of California Davis, 2825 50th Street, Sacramento, CA 95817; 4McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205; 5Section of Genetics, Department of Pediatrics, University of California Davis, Sacramento, CA 95817; 6Division of Pediatric Urology, The James Buchanan Brady Urological Institute, Johns Hopkins School of Medicine, Departments of 7Molecular Biology and Genetics, and 8Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA; 9Department of Chemistry and Biochemistry, Medical University, 2 Zdrave Street, Sofia 1431, Bulgaria; 10Department of Developmental Genetics, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin; 11Institute of Medical Genetics, Charite-University Medicine Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, D-12203 Berlin; 12Institute of Human Genetics, 13Department of Genomics, Life and Brain Center, 14Department of Neonatology, Children's Hospital, 15Department of Clinical Chemistry and Clinical Pharmacology, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany Received October 20, 2010; Accepted December 9, 2010 DOI: 10.3892/ijmm.2011.654 Abstract.
    [Show full text]