DOCSLIB.ORG

Identification of Genes Associated with Castration‑Resistant Prostate Cancer by Gene Expression Profile Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Identification of Genes Associated with Castration‑Resistant Prostate Cancer by Gene Expression Profile Analysis MOLECULAR MEDICINE REPORTS 16: 6803-6813, 2017 Identification of genes associated with castration‑resistant prostate cancer by gene expression profile analysis CHUI GUO HUANG1, FENG XI LI2, SONG PAN3, CHANG BAO XU1, JUN QIANG DAI4 and XING HUA ZHAO1 1Department of Urology, The Second Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan 450014; 2Department of Gastrointestinal Glands Surgery, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi 530000; 3Department of Urology, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan 450014; 4Department of Neurosurgery, The Second Affiliated Hospital, Lanzhou University, Lanzhou, Gansu 730030, P.R. China Received December 16, 2016; Accepted July 17, 2017 DOI: 10.3892/mmr.2017.7488 Abstract. Prostate cancer (CaP) is a serious and common western countries (1). CaP growth is driven by androgens; genital tumor. Generally, men with metastatic CaP can easily therefore, the conventional treatment option is to lower the develop castration-resistant prostate cancer (CRPC). However, levels of male sex hormones (2). Current treatment strate- the pathogenesis and tumorigenic pathways of CRPC remain to gies for CaP include surgery, external beam radiotherapy, be elucidated. The present study performed a comprehensive brachytherapy, chemotherapy and androgen-deprivation analysis on the gene expression profile of CRPC in order to therapy (ADT) (2,3). ADT often involves surgically determine the pathogenesis and tumorigenic of CRPC. The removing the testicles or using drugs to block the andro- GSE33316 microarray, which consisted of 5 non-castrated gens from affecting the body (4). Unfortunately, ADT has samples and 5 castrated samples, was downloaded from the gene a limited role in preventing majority of patients progressing expression omnibus database. Subsequently, 201 upregulated to castration-resistant prostate cancer (CRPC) and patients and 161 downregulated differentially expressed genes (DEGs) with metastatic prostate cancer progress to resistance of were identified using the limma package in R and those genes ADT (4,5). However, previous studies have been unable to were classified and annotated by plugin Mcode of Cytoscape. identity a mainstay cancer therapy. Thus, the mortality of Gene ontology (GO) and Kyoto Encyclopedia of Genes patients with CRPC continues to be very high (6). and Genomes (KEGG) pathway enrichment analyses were Previous studies have investigated novel molecular thera- performed using Database for Annotation, Visualization and peutic targets in CRPC and the influence that tumor metastasis Integrated Discovery and KEGG Orthology Based Annotation has on the biological processes. Activation of the androgen System 2.0 online tools to investigate the function of different receptor (AR) may stimulate tumor progression and prolif- gene modules. The BiNGO tool was used to visualize the level eration in prostate cancer (7), Koivisto et al (8) determined of enriched GO terms. Protein‑protein interaction network was that the activity of cytochrome P450 (CYP) may increase AR constructed using STRING and analyzed with Cytoscape. In gene amplification and continuous production of testosterone; conclusion, the present study determined that aldo‑keto reduc- therefore, the serum androgen levels following castration tase 3, cyclin B2, regulator of G protein signaling 2, nuclear can still contribute to prostate cancer cell growth and resis- factor of activated T‑cells and protein kinase C a may have tance. Seruga et al (9) determined that there are three core important roles in the development of CRPC. genes, including human epidermal growth factor receptor 2, transforming growth factor β (TGF-β) and the kinase of SRC Introduction family that are able to activate AR transduction pathways without androgen to stimulate. Chaux et al (10) suggested that Prostate cancer (CaP), as the most common genital neoplasm, the loss or mutation of tensin homolog have been implicated holds the highest incidence among men in the majority of in unlike properties of aggressive prostate cancer, including increasing the risk of biochemical relapse, reducing the time to metastasis and increasing the death rate in high‑risk cohorts of men. However, the molecular mechanisms with oncogenes or tumor suppressors that modulate the levels of critical proteins Correspondence to: Dr Xing Hua Zhao, Department of Urology, The Second Affiliated Hospital, Zhengzhou University, 2 Jingba remain unclear and their relevance in human disease and Road, Zhengzhou, Henan 450014, P.R. China therapy require further investigation. E-mail: [email protected] In order to provide novel mechanistic insights associ- ated with possible endogenous metastatic pathways in an Key words: castration-resistant prostate cancer, protein-protein androgen-deprived environment, the present study used the interaction network, differentially expressed genes, module analysis data from the gene expression profile available provided by Sun et al (11) that used the microarray of human pros- tate cancer xenograft model-LuCaP35 to analyze the gene 6804 HUANG et al: POTENTIAL BIOMARKERS OF CASTRATION-RESISTANT PROSTATE CANCER Table I. Top 10 upregulated and downregulated genes. A, Upregulated Gene symbol logFC Adjusted P-value F3 4.863582313 2.91x10-3 HAGLROS 4.847973534 9.32x10-6 GIMAP7 4.839624566 4.72x10-3 CEL 4.793641494 5.79x10-4 GPAT3 4.585119916 3.68x10-3 FAM3D 4.358475955 2.36x10-3 PDGFRL 3.929026478 1.09x10-3 RGS2 3.899872367 3.01x10-3 UPK3A 3.868345004 5.79x10-4 SI 3.843651777 7.42x10-4 B, Downregulated Gene symbol logFC Adjusted P-value MYBPC1 -4.622857598 7.75x10-3 LAMA1 ‑4.215584491 1.45x10-3 S100P ‑4.16482084 9.77x10-3 LEFTY1 ‑4.151682649 8.22x10-3 DEFB1 ‑3.863896718 1.59x10-3 LOX ‑3.400271316 1.06x10-3 DHRS2 ‑3.381711553 2.59x10-3 CCK ‑3.328128227 1.53x10-3 KRT19 ‑3.323100902 6.53x10-3 ANGPYL4 -3.29177361 9.74x10-3 FC, fold-change. Figure 1. Hierarchical clustering heat maps of the DEGs. The horizontal axis represents sample names, with GSM823845, GSM823846, GSM823847, GSM823851 and GSM823852 being the castration samples, and GSM823844, GSM823848, GSM823849, GSM823850 and GSM823853 the non-castration samples. The vertical axis indicates the clusters of DEGs: Red color stands Figure 2. Top 30 hub genes in from the constructed protein-protein inter- for an expression level above the mean and green color stands for the expres- action network. The horizontal axis represents degree. The vertical axis sion lower than the mean. DEGs, differentially expressed genes. indicated hub genes. MOLECULAR MEDICINE REPORTS 16: 6803-6813, 2017 6805 Figure 3. Protein‑protein interaction network constructed from the DEGs. Red circles indicate upregulated DEGs and green circles indicate downregulated DEGs. DEGs, differentially expressed genes. expression changes between 5 non-castrated and 5 castrated prostate cancer xenograft model-LuCaP35, included 5 controlled men. The current study identified 201 upregulated differen- samples from non-castrated men (GSM823844, GSM823848, tially expressed genes (DEGs) and 161 downregulated DEGs GSM823849, GSM823850, GSM823853) and 5 samples from using a stricter threshold of false discovery rate (FDR)<0.05 castrated men (GSM823845, GSM823846, GSM823847, and |log2 fold-change (FC)|>1.5. The visual protein-to-protein GSM823851, GSM823852). interaction (PPI) network was constructed using Cytoscape and the modules were identified with the Mcode plugin. Differentially expressed gene analysis. The series matrix file The present study used Gene Ontology (GO) and Kyoto was downloaded and a log2 transformation was performed. Encyclopedia of Genes and Genomes (KEGG) pathway to All sample data was normalized using the limma package analyze the potential interactions and functions of DEGs. in R software version 3.3.0 (https://www.r-project.org/) (12). In conclusion, the present study identified key genes which The DEGs were obtained with thresholds of |logFC|>1.5 and may have the potential to be biomarkers of tumorigenesis in P<0.05, using linear models and empirical Bayes methods for patients with CRPC. assessing differential expression in microarray experiments. Finally, a clustering analysis was performed using the DEGs Materials and methods and a heatmap of different groups including castrated and non-castrated was constructed (Fig. 1). Microarray data. The transcription profile of GSE33316 was downloaded from the National Center for Biotechnology Construction of PPI network and module analysis. In order Information Gene Expression Omnibus (GEO) database to predict protein interactions, which include physical and (www.ncbi.nlm.nih.gov/geo/) (11). The data used a human functional associations the present study used the Search Tool 6806 HUANG et al: POTENTIAL BIOMARKERS OF CASTRATION-RESISTANT PROSTATE CANCER Figure 4. Topology parameters of protein‑protein interaction networks. (A) Average clustering coefficient distribution. (B) Closeness centrality. (C) Neighborhood connectivity distribution. (D) Node‑degree distribution. (E) Shortest path length distribution. (F) Topological coefficients. for the Retrieval of Interacting Genes (STRING) to construct Results the PPI network for DEGs (minimum required interaction score >0.4) (13). In addition, Cytoscape software version 3.4.0 Identification of DEGs. Using microarray expression (http://cytoscape.org/download_old_versions.html) was used profiling from GEO database, the present study identified for visualization of the PPI networks. Following
Load more

Recommended publications
  • Genetic Analysis of Retinopathy in Type 1 Diabetes
    Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194).
    [Show full text]
  • Entrez Symbols Name Termid Termdesc 117553 Uba3,Ube1c
    Entrez Symbols Name TermID TermDesc 117553 Uba3,Ube1c ubiquitin-like modifier activating enzyme 3 GO:0016881 acid-amino acid ligase activity 299002 G2e3,RGD1310263 G2/M-phase specific E3 ubiquitin ligase GO:0016881 acid-amino acid ligase activity 303614 RGD1310067,Smurf2 SMAD specific E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 308669 Herc2 hect domain and RLD 2 GO:0016881 acid-amino acid ligase activity 309331 Uhrf2 ubiquitin-like with PHD and ring finger domains 2 GO:0016881 acid-amino acid ligase activity 316395 Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 361866 Hace1 HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1 GO:0016881 acid-amino acid ligase activity 117029 Ccr5,Ckr5,Cmkbr5 chemokine (C-C motif) receptor 5 GO:0003779 actin binding 117538 Waspip,Wip,Wipf1 WAS/WASL interacting protein family, member 1 GO:0003779 actin binding 117557 TM30nm,Tpm3,Tpm5 tropomyosin 3, gamma GO:0003779 actin binding 24779 MGC93554,Slc4a1 solute carrier family 4 (anion exchanger), member 1 GO:0003779 actin binding 24851 Alpha-tm,Tma2,Tmsa,Tpm1 tropomyosin 1, alpha GO:0003779 actin binding 25132 Myo5b,Myr6 myosin Vb GO:0003779 actin binding 25152 Map1a,Mtap1a microtubule-associated protein 1A GO:0003779 actin binding 25230 Add3 adducin 3 (gamma) GO:0003779 actin binding 25386 AQP-2,Aqp2,MGC156502,aquaporin-2aquaporin 2 (collecting duct) GO:0003779 actin binding 25484 MYR5,Myo1e,Myr3 myosin IE GO:0003779 actin binding 25576 14-3-3e1,MGC93547,Ywhah
    [Show full text]
  • Bioinformatics Analyses of Genomic Imprinting
    Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________
    [Show full text]
  • Congenital Disorders of Glycosylation from a Neurological Perspective
    brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations
    [Show full text]
  • Supp Material.Pdf
    Simon et al. Supplementary information: Table of contents p.1 Supplementary material and methods p.2-4 • PoIy(I)-poly(C) Treatment • Flow Cytometry and Immunohistochemistry • Western Blotting • Quantitative RT-PCR • Fluorescence In Situ Hybridization • RNA-Seq • Exome capture • Sequencing Supplementary Figures and Tables Suppl. items Description pages Figure 1 Inactivation of Ezh2 affects normal thymocyte development 5 Figure 2 Ezh2 mouse leukemias express cell surface T cell receptor 6 Figure 3 Expression of EZH2 and Hox genes in T-ALL 7 Figure 4 Additional mutation et deletion of chromatin modifiers in T-ALL 8 Figure 5 PRC2 expression and activity in human lymphoproliferative disease 9 Figure 6 PRC2 regulatory network (String analysis) 10 Table 1 Primers and probes for detection of PRC2 genes 11 Table 2 Patient and T-ALL characteristics 12 Table 3 Statistics of RNA and DNA sequencing 13 Table 4 Mutations found in human T-ALLs (see Fig. 3D and Suppl. Fig. 4) 14 Table 5 SNP populations in analyzed human T-ALL samples 15 Table 6 List of altered genes in T-ALL for DAVID analysis 20 Table 7 List of David functional clusters 31 Table 8 List of acquired SNP tested in normal non leukemic DNA 32 1 Simon et al. Supplementary Material and Methods PoIy(I)-poly(C) Treatment. pIpC (GE Healthcare Lifesciences) was dissolved in endotoxin-free D-PBS (Gibco) at a concentration of 2 mg/ml. Mice received four consecutive injections of 150 μg pIpC every other day. The day of the last pIpC injection was designated as day 0 of experiment.
    [Show full text]
  • Current Trends in Gene Recovery Mediated by the CRISPR-Cas System Hyeon-Ki Jang 1, Beomjong Song2,Gue-Hohwang1 and Sangsu Bae 1
    Jang et al. Experimental & Molecular Medicine (2020) 52:1016–1027 https://doi.org/10.1038/s12276-020-0466-1 Experimental & Molecular Medicine REVIEW ARTICLE Open Access Current trends in gene recovery mediated by the CRISPR-Cas system Hyeon-Ki Jang 1, Beomjong Song2,Gue-HoHwang1 and Sangsu Bae 1 Abstract The CRISPR-Cas system has undoubtedly revolutionized the genome editing field, enabling targeted gene disruption, regulation, and recovery in a guide RNA-specific manner. In this review, we focus on currently available gene recovery strategies that use CRISPR nucleases, particularly for the treatment of genetic disorders. Through the action of DNA repair mechanisms, CRISPR-mediated DNA cleavage at a genomic target can shift the reading frame to correct abnormal frameshifts, whereas DNA cleavage at two sites, which can induce large deletions or inversions, can correct structural abnormalities in DNA. Homology-mediated or homology-independent gene recovery strategies that require donor DNAs have been developed and widely applied to precisely correct mutated sequences in genes of interest. In contrast to the DNA cleavage-mediated gene correction methods listed above, base-editing tools enable base conversion in the absence of donor DNAs. In addition, CRISPR-associated transposases have been harnessed to generate a targeted knockin, and prime editors have been developed to edit tens of nucleotides in cells. Here, we introduce currently developed gene recovery strategies and discuss the pros and cons of each. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Introduction differs from that of the endogenous gene2. Furthermore, Human genetic disorders, often associated with severe the mutated endogenous gene, which is malfunctional and pathological phenotypes, are caused by genomic aberra- potentially cytotoxic, might still be transcribed.
    [Show full text]
  • A Curated Gene List for Reporting Results of Newborn Genomic Sequencing
    © American College of Medical Genetics and Genomics ORIGINAL RESEARCH ARTICLE A curated gene list for reporting results of newborn genomic sequencing Ozge Ceyhan-Birsoy, PhD1,2,3, Kalotina Machini, PhD1,2,3, Matthew S. Lebo, PhD1,2,3, Tim W. Yu, MD3,4,5, Pankaj B. Agrawal, MD, MMSC3,4,6, Richard B. Parad, MD, MPH3,7, Ingrid A. Holm, MD, MPH3,4, Amy McGuire, PhD8, Robert C. Green, MD, MPH3,9,10, Alan H. Beggs, PhD3,4, Heidi L. Rehm, PhD1,2,3,10; for the BabySeq Project Purpose: Genomic sequencing (GS) for newborns may enable detec- of newborn GS (nGS), and used our curated list for the first 15 new- tion of conditions for which early knowledge can improve health out- borns sequenced in this project. comes. One of the major challenges hindering its broader application Results: Here, we present our curated list for 1,514 gene–disease is the time it takes to assess the clinical relevance of detected variants associations. Overall, 954 genes met our criteria for return in nGS. and the genes they impact so that disease risk is reported appropri- This reference list eliminated manual assessment for 41% of rare vari- ately. ants identified in 15 newborns. Methods: To facilitate rapid interpretation of GS results in new- Conclusion: Our list provides a resource that can assist in guiding borns, we curated a catalog of genes with putative pediatric relevance the interpretive scope of clinical GS for newborns and potentially for their validity based on the ClinGen clinical validity classification other populations. framework criteria, age of onset, penetrance, and mode of inheri- tance through systematic evaluation of published evidence.
    [Show full text]
  • Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
    LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research.
    [Show full text]
  • Associated with Low Dehydrodolichol Diphosphate Synthase (DHDDS) Activity S
    Sabry et al. Orphanet Journal of Rare Diseases (2016) 11:84 DOI 10.1186/s13023-016-0468-1 RESEARCH Open Access A case of fatal Type I congenital disorders of glycosylation (CDG I) associated with low dehydrodolichol diphosphate synthase (DHDDS) activity S. Sabry1,2,3,4, S. Vuillaumier-Barrot1,2,5, E. Mintet1,2, M. Fasseu1,2, V. Valayannopoulos6, D. Héron7,8, N. Dorison8, C. Mignot7,8,9, N. Seta5,10, I. Chantret1,2, T. Dupré1,2,5 and S. E. H. Moore1,2* Abstract Background: Type I congenital disorders of glycosylation (CDG-I) are mostly complex multisystemic diseases associated with hypoglycosylated serum glycoproteins. A subgroup harbour mutations in genes necessary for the biosynthesis of the dolichol-linked oligosaccharide (DLO) precursor that is essential for protein N-glycosylation. Here, our objective was to identify the molecular origins of disease in such a CDG-Ix patient presenting with axial hypotonia, peripheral hypertonia, enlarged liver, micropenis, cryptorchidism and sensorineural deafness associated with hypo glycosylated serum glycoproteins. Results: Targeted sequencing of DNA revealed a splice site mutation in intron 5 and a non-sense mutation in exon 4 of the dehydrodolichol diphosphate synthase gene (DHDDS). Skin biopsy fibroblasts derived from the patient revealed ~20 % residual DHDDS mRNA, ~35 % residual DHDDS activity, reduced dolichol-phosphate, truncated DLO and N-glycans, and an increased ratio of [2-3H]mannose labeled glycoprotein to [2-3H]mannose labeled DLO. Predicted truncated DHDDS transcripts did not complement rer2-deficient yeast. SiRNA-mediated down-regulation of DHDDS in human hepatocellular carcinoma HepG2 cells largely mirrored the biochemical phenotype of cells from the patient.
    [Show full text]
  • Plasma Cells in Vitro Generation of Long-Lived Human
    Downloaded from http://www.jimmunol.org/ by guest on September 24, 2021 is online at: average * The Journal of Immunology , 32 of which you can access for free at: 2012; 189:5773-5785; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication November 2012; doi: 10.4049/jimmunol.1103720 http://www.jimmunol.org/content/189/12/5773 In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco, Sophie Stephenson, Matthew A. Care, Darren Newton, Nicholas A. Barnes, Adam Davison, Andy Rawstron, David R. Westhead, Gina M. Doody and Reuben M. Tooze J Immunol cites 65 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2012/11/16/jimmunol.110372 0.DC1 This article http://www.jimmunol.org/content/189/12/5773.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 24, 2021. The Journal of Immunology In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco,*,1 Sophie Stephenson,*,1 Matthew A.
    [Show full text]
  • Investigation of Differentially Expressed Genes in Nasopharyngeal Carcinoma by Integrated Bioinformatics Analysis
    916 ONCOLOGY LETTERS 18: 916-926, 2019 Investigation of differentially expressed genes in nasopharyngeal carcinoma by integrated bioinformatics analysis ZhENNING ZOU1*, SIYUAN GAN1*, ShUGUANG LIU2, RUjIA LI1 and jIAN hUANG1 1Department of Pathology, Guangdong Medical University, Zhanjiang, Guangdong 524023; 2Department of Pathology, The Eighth Affiliated hospital of Sun Yat‑sen University, Shenzhen, Guangdong 518033, P.R. China Received October 9, 2018; Accepted April 10, 2019 DOI: 10.3892/ol.2019.10382 Abstract. Nasopharyngeal carcinoma (NPC) is a common topoisomerase 2α and TPX2 microtubule nucleation factor), malignancy of the head and neck. The aim of the present study 8 modules, and 14 TFs were identified. Modules analysis was to conduct an integrated bioinformatics analysis of differ- revealed that cyclin-dependent kinase 1 and exportin 1 were entially expressed genes (DEGs) and to explore the molecular involved in the pathway of Epstein‑Barr virus infection. In mechanisms of NPC. Two profiling datasets, GSE12452 and summary, the hub genes, key modules and TFs identified in GSE34573, were downloaded from the Gene Expression this study may promote our understanding of the pathogenesis Omnibus database and included 44 NPC specimens and of NPC and require further in-depth investigation. 13 normal nasopharyngeal tissues. R software was used to identify the DEGs between NPC and normal nasopharyngeal Introduction tissues. Distributions of DEGs in chromosomes were explored based on the annotation file and the CYTOBAND database Nasopharyngeal carcinoma (NPC) is a common malignancy of DAVID. Gene ontology (GO) and Kyoto Encyclopedia of occurring in the head and neck. It is prevalent in the eastern Genes and Genomes (KEGG) pathway enrichment analysis and southeastern parts of Asia, especially in southern China, were applied.
    [Show full text]
  • Open Data for Differential Network Analysis in Glioma
    International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.
    [Show full text]