DOCSLIB.ORG

Role for High-Affinity Ige Receptor in Prognosis of Lung Adenocarcinoma Patients

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Role for High-Affinity Ige Receptor in Prognosis of Lung Adenocarcinoma Patients Published OnlineFirst August 3, 2017; DOI: 10.1158/2326-6066.CIR-16-0392 Research Article Cancer Immunology Research Role for High-Affinity IgE Receptor in Prognosis of Lung Adenocarcinoma Patients Dalam Ly1,2, Chang-Qi Zhu3, Michael Cabanero3, Ming-Sound Tsao3,4, and Li Zhang1,2,4 Abstract Cancer development and biology is influenced by the host 10 additional independently published microarray datasets of immune system. Emerging data indicate that the context of lung adenocarcinoma [n ¼ 1,097; overall survival hazard ratio immune cell infiltrates may contribute to cancer prognosis. (HR), 2.05; 95% confidence interval, 1.64–2.56; P < 0.0001] However, the types of infiltrating immune cells that are critical and was found to be an independent prognostic indicator for cancer development remain controversial. In attempts to relative to tumor stage (overall survival HR, 2.09, 95% con- gain insights into the immune networks that regulate and/or fidence interval, 1.65–2.66; P < 0.0001). Network analysis predict tumor progression, gene expression analysis was con- revealed that genes associated with Fce complex (FCER1, ducted on microarray datasets of resected tumor samples from MS4A2) formed the largest and most significant pathway of 128 early-stage non–small cell lung cancer (NSCLC) adeno- the signature. Using immunohistochemistry, we validated that carcinoma patients. By limiting analysis to immune-related MS4A2, the b subunit of the IgE receptor expressed on mast genes, we identified a 9-gene signature using MAximizing R cells, is a favorable prognostic indicator and show that MS4A2 Square Algorithm that selected for the greatest separation gene expression is an independent prognostic marker for between favorable and adverse prognostic patient subgroups. early-stage lung cancer patient survival. Cancer Immunol Res; 5(9); The prognostic value of this 9-gene signature was validated in 821–9. Ó2017 AACR. Introduction immune cell subsets can be found within lung tumors and many influence NSCLC prognosis (6–9). Lung cancer is a leading cause of cancer death, with approxi- þ þ High densities of CD8 T cells, dendritic cells, and Th1 CD4 T mately 85% being non–small cell lung cancer (NSCLC). Adeno- cells are correlated with beneficial prognosis, whereas regulatory carcinoma (ADC) is the most common form of NSCLC. Complete T cells (Treg) have been shown to have detrimental prognostic surgical resection is the standard treatment for early-stage I and II effects (6, 7, 9). Though granulocytic populations, such as neu- NSCLC patients, but up to 50% of these patients experience post- trophils, mast cells, and eosinophils make up a large part of surgical recurrence (1). Cancer development is influenced by its resident innate immune cells in the lung, their role in NSCLC microenvironment through interactions between cancer cells, prognosis remains unclear or contradictory. High densities of stroma, vasculature and immune infiltrates (2, 3). Histologic neutrophils have been reported to have detrimental effects or no evidence indicates that the presence of intratumoral lymphoid prognostic impact (7), whereas reports on mast cell densities have structures and immune cell infiltration are prognostic markers for been contradictory, with studies showing both positive and NSCLC (4, 5). The number and types of tumor-infiltrating leu- negative prognostic roles (10, 11). With the clinical success of kocytes (TIL) present within the tumor microenvironment influ- therapies that target the immune system, an understanding of ence tumor growth and metastasis. Indeed, the majority of how immune cells contribute to the microenvironment would complement staging and treatment options (12, 13). Gene expression analysis has been used to reveal altered path- 1Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada. 2Department of Immunology, University of Toronto, Toronto, ways and regulators of NSCLC tumorigenesis, with some signa- Ontario, Canada. 3Princess Margaret Cancer Centre, University Health Network, tures found to include immune-associated genes involved in Toronto, Ontario, Canada. 4Departments of Laboratory Medicine and Pathobi- recruitment and survival of immune cell subsets (14, 15). How- ology, University of Toronto, Toronto, Ontario, Canada. ever, few signatures derived from resected tumor have described Note: Supplementary data for this article are available at Cancer Immunology immune-related genes associated with the prognosis of NSCLC Research Online (http://cancerimmunolres.aacrjournals.org/). patients (14). Using an analysis that limited microarray gene fi D. Ly and C.Q. Zhu contributed equally to this article. expression data to curated immune-related genes, we identi ed a prognostic 9-gene signature that reveals a favorable prognostic Corresponding Authors: Li Zhang, University of Toronto, MaRS Centre, Toronto fi Medical Discovery Tower, 2nd Floor Room 2-807, Toronto, Ontario M5G 1L7, role for expression of the high-af nity Fce receptor. Canada. Phone: 416-581-7521; Fax: 416-581-7515; E-mail: [email protected]; and Ming-Sound Tsao, Princess Margaret Cancer Centre, Toronto Medical Dis- Materials and Methods covery Tower, 101 College Street, Rm 14-401, Toronto, Ontario, M5G 1L7. Phone: Training and validation datasets 416-634-8722; E-mail: [email protected] The immune-related gene signature was derived using the doi: 10.1158/2326-6066.CIR-16-0392 microarray dataset of 128 resected samples of lung adenocarci- Ó2017 American Association for Cancer Research. noma patients who underwent surgical resection at University www.aacrjournals.org 821 Downloaded from cancerimmunolres.aacrjournals.org on September 29, 2021. © 2017 American Association for Cancer Research. Published OnlineFirst August 3, 2017; DOI: 10.1158/2326-6066.CIR-16-0392 Ly et al. Health Network (UHN, Toronto, ON) between 1995 and 2005 (27). Using this gene list, unsupervised hierarchical clustering (see Table 1 for demographics and analysis). This represents a using Genesis (28) showed two distinguishable clusters in the subset of 181 stage I–II NSCLC patients whose gene expression adenocarcinoma samples of the UHN training dataset. Probe sets data were used in a previously published prognostic signature that were significantly different between the two cluster were validation study and have been deposited with GEO (GSE50081; further introduced to a Cox proportional regression model for refs. 16, 17). None of the patients received adjuvant therapy their associations with 5-year survival. A P < 0.005 was considered (see Table 1 for demographics and analysis). The profiling was as significantly associated. For signature optimization, we used performed using the Affymetrix U133 2.0 Plus arrays. The data the Maximizing R Square Algorithm (MARSA; ref. 17) to select a were normalized using robust multiarray averaging. Patient sam- minimum set of genes that had the highest independent ability to ples were collected and analysis done under protocol 05-0221-T classify patients into high- and low-risk subgroups. The MARSA approved by the UHN Research Ethics Board. Signature validation started with an exclusion procedure, which took all preselected was performed using 10 additional published microarray datasets probe-sets (ps) significantly associated with univariate 5-year (18–26) of NSCLC resected samples for which overall and dis- survival (191 ps; Supplementary Table S3). An exclusion (back- eases-free survival data were available (n ¼ 1524; Supplementary ward) and an inclusion (forward) selection procedure were Table S1 for demographics). applied consecutively, the probe set against R-square value was plotted and a minimum number of probe sets (9 ps) having the Signature selection and validation largest c-statistic was identified. The "immune-related" gene list was obtained from the NIAID ImmPort Comprehensive List of Immune-Related Genes (version Gene annotation and functional categorization 2011_08_01) and mapped onto Affymetrix U133 Plus 2 array by The Uniprot database was used to assign all gene annotations querying Netaffy (https://www.affymetrix.com/analysis/netaffx/) and functions. For GO and functional interaction networks, the using gene symbol (Supplementary Table S2). The mapped gene 191 ps significantly associated with univariate 5-year survival were list contained 3,690 genes (6,437 probe sets) that where compiled analyzed using DAVID Bioinformatics Resources v.6.8 or with the using immune-related Gene Ontology (GO) keywords and Reactome FI network plugin in Cytoscape. Networks were con- included CD antigens, cytokines, chemokines, and their ligands sidered when at least two interacting partners where determined Table 1. Clinical–pathologic markers and association to the 9-gene signature 9-gene signature Total (%) Low risk (n ¼ 64) High risk (n ¼ 64) P Age <65 40 (31.2) 68.4 Æ 10.4 69.0 Æ 8.9 0.23 <65 88 (68.7) Gender Male 65 (50.7) 29 (45.3) 36 (56.2) 0.216 Female 63 (49.2) 35 (54.7) 28 (43.8) Smoking Never 23 (17.9) 16 (25.0) 7 (10.9) 0.064 Ex-smoker 56 (43.7) 30 (46.9) 26 (40.6) Current smoker 36 (28.1) 13 (20.3) 23 (35.9) Unknown 13 (10.1) 5 (7.8) 8 (12.5) Stage IA 36 (28.1) 27 (42.2) 9 (14.1) 0.0003 IB 56 (43.7) 25 (39.1) 31 (48.4) IIA 7 (5.4) 5 (7.8) 2 (3.1) IIB 29 (22.6) 7 (10.9) 22 (34.4) EGFR Wild-type 100 (78.1) 42 (65.6) 58 (90.6) <0.0001 Mutant 26 (20.3) 22 (34.4) 4 (6.3) Unknown 2 (1.5) 0 2 (3.1) KRAS Wild-type 87 (67.9) 47 (73.4) 40 (62.5) 0.228 Mutant 40 (31.2) 17 (26.6) 23 (35.9) Unknown 1 (0.8) 0 1 (1.6) Histological pattern Acinar 28 (21.9) 16 (25.0) 12 (18.7) 0.002 Lepidic 5 (3.9) 3 (4.7) 2 (3.1) Papillary 32 (25.0) 23 (35.9) 9 (14.1) Micropapillary 4 (3.1) 2 (3.1) 2 (3.1) Solid 27 (21.1) 5 (7.8) 22 (34.4) Unknown 32 (25) 15 (23.4) 17 (26.6) Resection type Segmentectomy 5 (3.9) 3 (2.3) 2 (1.6) 0.357 Bisegmentectomy 1 (0.78) 0 (0) 1 (0.78) Lobectomy 120 (93.7) 61 (47.6) 59 (46.1) Pneumonectomy 2 (1.6) 0 (0) 2 (1.6) 822 Cancer Immunol Res; 5(9) September 2017 Cancer Immunology Research Downloaded from cancerimmunolres.aacrjournals.org on September 29, 2021.
Load more

Recommended publications
  • Viewed Under 23 (B) Or 203 (C) fi M M Male Cko Mice, and Largely Unaffected Magni Cation; Scale Bars, 500 M (B) and 50 M (C)
    BRIEF COMMUNICATION www.jasn.org Renal Fanconi Syndrome and Hypophosphatemic Rickets in the Absence of Xenotropic and Polytropic Retroviral Receptor in the Nephron Camille Ansermet,* Matthias B. Moor,* Gabriel Centeno,* Muriel Auberson,* † † ‡ Dorothy Zhang Hu, Roland Baron, Svetlana Nikolaeva,* Barbara Haenzi,* | Natalya Katanaeva,* Ivan Gautschi,* Vladimir Katanaev,*§ Samuel Rotman, Robert Koesters,¶ †† Laurent Schild,* Sylvain Pradervand,** Olivier Bonny,* and Dmitri Firsov* BRIEF COMMUNICATION *Department of Pharmacology and Toxicology and **Genomic Technologies Facility, University of Lausanne, Lausanne, Switzerland; †Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts; ‡Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia; §School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia; |Services of Pathology and ††Nephrology, Department of Medicine, University Hospital of Lausanne, Lausanne, Switzerland; and ¶Université Pierre et Marie Curie, Paris, France ABSTRACT Tight control of extracellular and intracellular inorganic phosphate (Pi) levels is crit- leaves.4 Most recently, Legati et al. have ical to most biochemical and physiologic processes. Urinary Pi is freely filtered at the shown an association between genetic kidney glomerulus and is reabsorbed in the renal tubule by the action of the apical polymorphisms in Xpr1 and primary fa- sodium-dependent phosphate transporters, NaPi-IIa/NaPi-IIc/Pit2. However, the milial brain calcification disorder.5 How- molecular identity of the protein(s) participating in the basolateral Pi efflux remains ever, the role of XPR1 in the maintenance unknown. Evidence has suggested that xenotropic and polytropic retroviral recep- of Pi homeostasis remains unknown. Here, tor 1 (XPR1) might be involved in this process. Here, we show that conditional in- we addressed this issue in mice deficient for activation of Xpr1 in the renal tubule in mice resulted in impaired renal Pi Xpr1 in the nephron.
    [Show full text]
  • Table 2. Significant
    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
    [Show full text]
  • Single-Cell Profiling Identifies Impaired Adaptive NK Cells Expanded After HCMV Reactivation in Haploidentical HSCT
    Single-cell profiling identifies impaired adaptive NK cells expanded after HCMV reactivation in haploidentical HSCT Elisa Zaghi, … , Enrico Lugli, Domenico Mavilio JCI Insight. 2021;6(12):e146973. https://doi.org/10.1172/jci.insight.146973. Research Article Hematology Immunology Graphical abstract Find the latest version: https://jci.me/146973/pdf RESEARCH ARTICLE Single-cell profiling identifies impaired adaptive NK cells expanded after HCMV reactivation in haploidentical HSCT Elisa Zaghi,1 Michela Calvi,1,2 Simone Puccio,3 Gianmarco Spata,1 Sara Terzoli,1 Clelia Peano,4 Alessandra Roberto,3 Federica De Paoli,3 Jasper J.P. van Beek,3 Jacopo Mariotti,5 Chiara De Philippis,5 Barbara Sarina,5 Rossana Mineri,6 Stefania Bramanti,5 Armando Santoro,5 Vu Thuy Khanh Le-Trilling,7 Mirko Trilling,7 Emanuela Marcenaro,8 Luca Castagna,5 Clara Di Vito,1,2 Enrico Lugli,3,9 and Domenico Mavilio1,2 1Unit of Clinical and Experimental Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy. 2BIOMETRA, Università degli Studi di Milano, Milan, Italy. 3Laboratory of Translational Immunology, 4Institute of Genetic and Biomedical Research, UoS Milan, National Research Council, and Genomic Unit, 5Bone Marrow Transplant Unit, and 6Molecular Biology Section, Clinical Investigation Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy. 7Institute for Virology, University Hospital Essen, University Duisburg-Essen, Essen, Germany. 8Department of Experimental Medicine, University of Genoa, Genoa, Italy. 9Flow Cytometry Core, IRCCS Humanitas Research Hospital, Milan, Italy. Haploidentical hematopoietic stem cell transplantation (h-HSCT) represents an efficient curative approach for patients affected by hematologic malignancies in which the reduced intensity conditioning induces a state of immunologic tolerance between donor and recipient.
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]
  • Role of RUNX1 in Aberrant Retinal Angiogenesis Jonathan D
    Page 1 of 25 Diabetes Identification of RUNX1 as a mediator of aberrant retinal angiogenesis Short Title: Role of RUNX1 in aberrant retinal angiogenesis Jonathan D. Lam,†1 Daniel J. Oh,†1 Lindsay L. Wong,1 Dhanesh Amarnani,1 Cindy Park- Windhol,1 Angie V. Sanchez,1 Jonathan Cardona-Velez,1,2 Declan McGuone,3 Anat O. Stemmer- Rachamimov,3 Dean Eliott,4 Diane R. Bielenberg,5 Tave van Zyl,4 Lishuang Shen,1 Xiaowu Gai,6 Patricia A. D’Amore*,1,7 Leo A. Kim*,1,4 Joseph F. Arboleda-Velasquez*1 Author affiliations: 1Schepens Eye Research Institute/Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, 20 Staniford St., Boston, MA 02114 2Universidad Pontificia Bolivariana, Medellin, Colombia, #68- a, Cq. 1 #68305, Medellín, Antioquia, Colombia 3C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114 4Retina Service, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, 243 Charles St., Boston, MA 02114 5Vascular Biology Program, Boston Children’s Hospital, Department of Surgery, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115 6Center for Personalized Medicine, Children’s Hospital Los Angeles, Los Angeles, 4650 Sunset Blvd, Los Angeles, CA 90027, USA 7Department of Pathology, Harvard Medical School, 25 Shattuck St., Boston, MA 02115 Corresponding authors: Joseph F. Arboleda-Velasquez: [email protected] Ph: (617) 912-2517 Leo Kim: [email protected] Ph: (617) 912-2562 Patricia D’Amore: [email protected] Ph: (617) 912-2559 Fax: (617) 912-0128 20 Staniford St. Boston MA, 02114 † These authors contributed equally to this manuscript Word Count: 1905 Tables and Figures: 4 Diabetes Publish Ahead of Print, published online April 11, 2017 Diabetes Page 2 of 25 Abstract Proliferative diabetic retinopathy (PDR) is a common cause of blindness in the developed world’s working adult population, and affects those with type 1 and type 2 diabetes mellitus.
    [Show full text]
  • Anti-FCER1G (Aa 22-86) Polyclonal Antibody (DPABH-03673) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
    Anti-FCER1G (aa 22-86) polyclonal antibody (DPABH-03673) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Antigen Description Associates with a variety of FcR alpha chains to form a functional signaling complex. Regulates several aspects of the immune response. The gamma subunit has a critical role in allowing the IgE Fc receptor to reach the cell surface. Immunogen Synthetic peptide corresponding to a region within amino acids 22-86 of Human FCER1G. Isotype IgG Source/Host Rabbit Species Reactivity Human Purification Immunogen affinity purified Conjugate Unconjugated Applications WB, IHC-P Format Liquid Size 100 μl Buffer pH: 7.00; Constituents: 0.75% Glycine, 1.21% Tris, 10% Glycerol Preservative None Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. GENE INFORMATION Gene Name FCER1G Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide [ Homo sapiens ] Official Symbol FCER1G Synonyms FCER1G; Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide; high affinity immunoglobulin epsilon receptor subunit gamma; fcRgamma; fceRI gamma; fc-epsilon RI- gamma; Fc receptor gamma-chain; immunoglobulin E receptor, high affinity, gamma chain; FCRG; 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved Entrez Gene ID 2207 Protein Refseq NP_004097 UniProt ID P30273 Chromosome Location 1q23 Pathway Asthma; Cell surface interactions at the vascular wall; Fc epsilon RI signaling pathway; Fc- epsilon receptor I signaling in mast cells; GPVI-mediated activation cascade; Function IgE binding; IgG binding; receptor activity; transmembrane signaling receptor activity; 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 2 © Creative Diagnostics All Rights Reserved.
    [Show full text]
  • Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
    Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int.
    [Show full text]
  • Supplementary Figures and Tables
    SUPPLEMENTARY DATA Supplementary Figure 1. Isolation and culture of endothelial cells from surgical specimens of FVM. (A) Representative pre-surgical fundus photograph of a right eye exhibiting a FVM encroaching on the optic nerve (dashed line) causing tractional retinal detachment with blot hemorrhages throughout retina (arrow heads). (B) Magnetic beads (arrows) allow for separation and culturing of enriched cell populations from surgical specimens (scale bar = 100 μm). (C) Cultures of isolated cells stained positively for CD31 representing a successfully isolated enriched population (scale bar = 40 μm). ©2017 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-1035/-/DC1 SUPPLEMENTARY DATA Supplementary Figure 2. Efficient siRNA knockdown of RUNX1 expression and function demonstrated by qRT-PCR, Western Blot, and scratch assay. (A) RUNX1 siRNA induced a 60% reduction of RUNX1 expression measured by qRT-PCR 48 hrs post-transfection whereas expression of RUNX2 and RUNX3, the two other mammalian RUNX orthologues, showed no significant changes, indicating specificity of our siRNA. Functional inhibition of Runx1 signaling was demonstrated by a 330% increase in insulin-like growth factor binding protein-3 (IGFBP3) RNA expression level, a known target of RUNX1 inhibition. Western blot demonstrated similar reduction in protein levels. (B) siRNA- 2’s effect on RUNX1 was validated by qRT-PCR and western blot, demonstrating a similar reduction in both RNA and protein. Scratch assay demonstrates functional inhibition of RUNX1 by siRNA-2. ns: not significant, * p < 0.05, *** p < 0.001 ©2017 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-1035/-/DC1 SUPPLEMENTARY DATA Supplementary Table 1.
    [Show full text]
  • Microarray Analysis of Novel Genes Involved in HSV- 2 Infection
    Microarray analysis of novel genes involved in HSV- 2 infection Hao Zhang Nanjing University of Chinese Medicine Tao Liu ( [email protected] ) Nanjing University of Chinese Medicine https://orcid.org/0000-0002-7654-2995 Research Article Keywords: HSV-2 infection,Microarray analysis,Histospecic gene expression Posted Date: May 12th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-517057/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/19 Abstract Background: Herpes simplex virus type 2 infects the body and becomes an incurable and recurring disease. The pathogenesis of HSV-2 infection is not completely clear. Methods: We analyze the GSE18527 dataset in the GEO database in this paper to obtain distinctively displayed genes(DDGs)in the total sequential RNA of the biopsies of normal and lesioned skin groups, healed skin and lesioned skin groups of genital herpes patients, respectively.The related data of 3 cases of normal skin group, 4 cases of lesioned group and 6 cases of healed group were analyzed.The histospecic gene analysis , functional enrichment and protein interaction network analysis of the differential genes were also performed, and the critical components were selected. Results: 40 up-regulated genes and 43 down-regulated genes were isolated by differential performance assay. Histospecic gene analysis of DDGs suggested that the most abundant system for gene expression was the skin, immune system and the nervous system.Through the construction of core gene combinations, protein interaction network analysis and selection of histospecic distribution genes, 17 associated genes were selected CXCL10,MX1,ISG15,IFIT1,IFIT3,IFIT2,OASL,ISG20,RSAD2,GBP1,IFI44L,DDX58,USP18,CXCL11,GBP5,GBP4 and CXCL9.The above genes are mainly located in the skin, immune system, nervous system and reproductive system.
    [Show full text]
  • Supplementary Table 2
    Supplementary Table 2. Differentially Expressed Genes following Sham treatment relative to Untreated Controls Fold Change Accession Name Symbol 3 h 12 h NM_013121 CD28 antigen Cd28 12.82 BG665360 FMS-like tyrosine kinase 1 Flt1 9.63 NM_012701 Adrenergic receptor, beta 1 Adrb1 8.24 0.46 U20796 Nuclear receptor subfamily 1, group D, member 2 Nr1d2 7.22 NM_017116 Calpain 2 Capn2 6.41 BE097282 Guanine nucleotide binding protein, alpha 12 Gna12 6.21 NM_053328 Basic helix-loop-helix domain containing, class B2 Bhlhb2 5.79 NM_053831 Guanylate cyclase 2f Gucy2f 5.71 AW251703 Tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 5.57 NM_021691 Twist homolog 2 (Drosophila) Twist2 5.42 NM_133550 Fc receptor, IgE, low affinity II, alpha polypeptide Fcer2a 4.93 NM_031120 Signal sequence receptor, gamma Ssr3 4.84 NM_053544 Secreted frizzled-related protein 4 Sfrp4 4.73 NM_053910 Pleckstrin homology, Sec7 and coiled/coil domains 1 Pscd1 4.69 BE113233 Suppressor of cytokine signaling 2 Socs2 4.68 NM_053949 Potassium voltage-gated channel, subfamily H (eag- Kcnh2 4.60 related), member 2 NM_017305 Glutamate cysteine ligase, modifier subunit Gclm 4.59 NM_017309 Protein phospatase 3, regulatory subunit B, alpha Ppp3r1 4.54 isoform,type 1 NM_012765 5-hydroxytryptamine (serotonin) receptor 2C Htr2c 4.46 NM_017218 V-erb-b2 erythroblastic leukemia viral oncogene homolog Erbb3 4.42 3 (avian) AW918369 Zinc finger protein 191 Zfp191 4.38 NM_031034 Guanine nucleotide binding protein, alpha 12 Gna12 4.38 NM_017020 Interleukin 6 receptor Il6r 4.37 AJ002942
    [Show full text]
  • Haplotype Analysis in Multiple Crosses to Identify a QTL Gene
    Downloaded from genome.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Haplotype Analysis in Multiple Crosses to Identify a QTL Gene Xiaosong Wang,1 Ron Korstanje, David Higgins, and Beverly Paigen The Jackson Laboratory, Bar Harbor, Maine 04609, USA Identifying quantitative trait locus (QTL) genes is a challenging task. Herein, we report using a two-step process to identify Apoa2 as the gene underlying Hdlq5, a QTL for plasma high-density lipoprotein cholesterol (HDL) levels on mouse chromosome 1. First, we performed a sequence analysis of the Apoa2 coding region in 46 genetically diverse mouse strains and found five different APOA2 protein variants, which we named APOA2a to APOA2e. Second, we conducted a haplotype analysis of the strains in 21 crosses that have so far detected HDL QTLs; we found that Hdlq5 was detected only in the nine crosses where one parent had the APOA2b protein variant characterized by an Ala61-to-Val61 substitution. We then found that strains with the APOA2b variant had significantly higher (P Յ 0.002) plasma HDL levels than those with either the APOA2a or the APOA2c variant. These findings support Apoa2 as the underlying Hdlq5 gene and suggest the Apoa2 polymorphisms responsible for the Hdlq5 phenotype. Therefore, haplotype analysis in multiple crosses can be used to support a candidate QTL gene. [Supplemental material is available online at www.genome.org. The following individuals kindly provided reagents, samples, or unpublished information as indicated in the paper: D. Puppione.] Most common human diseases, such as atherosclerosis, diabetes, 46 genetically diverse mouse strains, and a haplotype analysis of and obesity, are complex traits determined by many genetic and strains both in the nine crosses that detected Hdlq5 and in the 12 environmental factors.
    [Show full text]
  • Bioinformatic Analysis Reveals Novel Hub Genes Pathways Associated
    Bioinformatic Analysis Reveals Novel Hub Genes pathways associated with IgA nephropathy xue Jiang Hangzhou Hospital of Traditional Chinese Medicine Zhijie Xu Zhejiang University School of Medicine First Aliated Hospital Yuanyuan Du Hangzhou Hospital of Traditional Chinese Medicine Hongyu Chen ( [email protected] ) Hangzhou Hospital of Traditional Chinese Medicine https://orcid.org/0000-0002-8207-1421 Research Keywords: IgA nephropathy, Gene Expression Proling, Bioinformatic Analysis Posted Date: May 22nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-29974/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on September 7th, 2020. See the published version at https://doi.org/10.1186/s40001-020-00441-2. Page 1/18 Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. The aim of the study is to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray data GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identied. With DEGs, we further performed a series of functional enrichment analyses. Protein- protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, then further identied the hub gene and most important module in DEGs, Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN.
    [Show full text]