Function of Aquaporins in Sepsis: a Systematic Review Katharina Rump* and Michael Adamzik
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Activity-Dependent Modulation of Neuronal Sodium Channel Expression Joshua Peter Klein Yale University
Yale University EliScholar – A Digital Platform for Scholarly Publishing at Yale Yale Medicine Thesis Digital Library School of Medicine 5-2004 Activity-dependent modulation of neuronal sodium channel expression Joshua Peter Klein Yale University. Follow this and additional works at: http://elischolar.library.yale.edu/ymtdl Part of the Medicine and Health Sciences Commons Recommended Citation Klein, Joshua Peter, "Activity-dependent modulation of neuronal sodium channel expression" (2004). Yale Medicine Thesis Digital Library. 2198. http://elischolar.library.yale.edu/ymtdl/2198 This Open Access Dissertation is brought to you for free and open access by the School of Medicine at EliScholar – A Digital Platform for Scholarly Publishing at Yale. It has been accepted for inclusion in Yale Medicine Thesis Digital Library by an authorized administrator of EliScholar – A Digital Platform for Scholarly Publishing at Yale. For more information, please contact [email protected]. ACTIVITY-DEPENDENT MODULATION OF NEURONAL SODIUM CHANNEL EXPRESSION A Dissertation Presented to the Faculty of the Graduate School of Yale University in Candidacy for the Degree of Doctor of Philosophy by Joshua Peter Klein Dissertation Director: Stephen G. Waxman, M.D.,Ph.D. May, 2004 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACTiViTY-DEPENDENT MODULATION OF NEURONAL SODIUM CHANNEL EXPRESSION Joshua Peter Klein 2004 ABSTRACT Action potentials initiate via the voltage-dependent opening of plasma membrane-associated sodium channels. The number and type of sodium channels in a neuronal membrane determine the quantity of sodium current that results from a given stimulus. The expression of sodium channels in neurons is plastic, and is not only altered by injury and disease, but also by subtle changes in physiologic environment. -
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 -
Aquaporin Channels in the Heart—Physiology and Pathophysiology
International Journal of Molecular Sciences Review Aquaporin Channels in the Heart—Physiology and Pathophysiology Arie O. Verkerk 1,2,* , Elisabeth M. Lodder 2 and Ronald Wilders 1 1 Department of Medical Biology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] 2 Department of Experimental Cardiology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected]; Tel.: +31-20-5664670 Received: 29 March 2019; Accepted: 23 April 2019; Published: 25 April 2019 Abstract: Mammalian aquaporins (AQPs) are transmembrane channels expressed in a large variety of cells and tissues throughout the body. They are known as water channels, but they also facilitate the transport of small solutes, gasses, and monovalent cations. To date, 13 different AQPs, encoded by the genes AQP0–AQP12, have been identified in mammals, which regulate various important biological functions in kidney, brain, lung, digestive system, eye, and skin. Consequently, dysfunction of AQPs is involved in a wide variety of disorders. AQPs are also present in the heart, even with a specific distribution pattern in cardiomyocytes, but whether their presence is essential for proper (electro)physiological cardiac function has not intensively been studied. This review summarizes recent findings and highlights the involvement of AQPs in normal and pathological cardiac function. We conclude that AQPs are at least implicated in proper cardiac water homeostasis and energy balance as well as heart failure and arsenic cardiotoxicity. However, this review also demonstrates that many effects of cardiac AQPs, especially on excitation-contraction coupling processes, are virtually unexplored. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
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. -
Hereditary Kidney Disorders
A. Stavljenić-Rukavina Hereditary kidney disorders How to Cite this article: Hereditary Kidney Disorders- eJIFCC 20/01 2009 http://www.ifcc.org 5. HEREDITARY KIDNEY DISORDERS Ana Stavljenić-Rukavina 5.1 Introduction Hereditary kidney disorders represent significant risk for the development of end stage renal desease (ESRD). Most of them are recognized in childhood, or prenataly particularly those phenotypicaly expressed as anomalies on ultrasound examination (US) during pregnancy. They represent almost 50% of all fetal malformations detected by US (1). Furthermore many of urinary tract malformations are associated with renal dysplasia which leeds to renal failure. Recent advances in molecular genetics have made a great impact on better understanding of underlying molecular mechanisms in different kidney and urinary tract disorders found in childhood or adults. Even some of clinical syndromes were not recognized earlier as genetic one. In monogenic kidney diseases gene mutations have been identified for Alport syndrome and thin basement membrane disease, autosomal dominant polycystic kidney disease, and tubular transporter disorders. There is evident progress in studies of polygenic renal disorders as glomerulopathies and diabetic nephropathy. The expanded knowledge on renal physiology and pathophysiology by analyzing the phenotypes caused by defected genes might gain to earlier diagnosis and provide new diagnostic and prognostic tool. The global increasing number of patients with ESRD urges the identification of molecular pathways involved in renal pathophysiology in order to serve as targets for either prevention or intervention. Molecular genetics nowadays possess significant tools that can be used to identify genes involved in renal disease including gene expression arrays, linkage analysis and association studies. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Patient-Based Cross-Platform Comparison of Oligonucleotide Microarray Expression Profiles
Laboratory Investigation (2005) 85, 1024–1039 & 2005 USCAP, Inc All rights reserved 0023-6837/05 $30.00 www.laboratoryinvestigation.org Patient-based cross-platform comparison of oligonucleotide microarray expression profiles Joerg Schlingemann1,*, Negusse Habtemichael2,*, Carina Ittrich3, Grischa Toedt1, Heidi Kramer1, Markus Hambek4, Rainald Knecht4, Peter Lichter1, Roland Stauber2 and Meinhard Hahn1 1Division of Molecular Genetics, Deutsches Krebsforschungszentrum, Heidelberg, Germany; 2Chemotherapeutisches Forschungsinstitut Georg-Speyer-Haus, Frankfurt am Main, Germany; 3Central Unit Biostatistics, Deutsches Krebsforschungszentrum, Heidelberg, Germany and 4Department of Otorhinolaryngology, Universita¨tsklinik, Johann-Wolfgang-Goethe-Universita¨t Frankfurt, Frankfurt, Germany The comparison of gene expression measurements obtained with different technical approaches is of substantial interest in order to clarify whether interplatform differences may conceal biologically significant information. To address this concern, we analyzed gene expression in a set of head and neck squamous cell carcinoma patients, using both spotted oligonucleotide microarrays made from a large collection of 70-mer probes and commercial arrays produced by in situ synthesis of sets of multiple 25-mer oligonucleotides per gene. Expression measurements were compared for 4425 genes represented on both platforms, which revealed strong correlations between the corresponding data sets. Of note, a global tendency towards smaller absolute ratios was observed when -
Cdk15 Igfals Lingo4 Gjb3 Tpbg Lrrc38 Serpinf1 Apod Trp73 Lama4 Chrnd Col9a1col11a1col5a2 Fgl2 Pitx2 Col2a1 Col3a1 Lamb3 Col24a1
Bnc2 Wdr72 Ptchd1 Abtb2 Spag5 Zfp385a Trim17 Ier2 Il1rapl1 Tpd52l1 Fam20a Car8 Syt5 Plxnc1 Sema3e Ndrg4 Snph St6galnac5 Mcpt2 B3galt2 Sphkap Arhgap24 Prss34 Lhfpl2 Ermap Rnf165 Shroom1 Grm4 Mobp Dock2 Tmem9b Slc35d3 Otud7b Serpinb3a Sh3d19 Syt6 Zan Trim67 Clec18a Mcoln1 Tob1 Slc45a2 Pcdhb9 Pcdh17 Plscr1 Gpr143 Cela1 Frem1 Sema3f Lgi2 Igsf9 Fjx1 Cpne4 Adgb Depdc7 Gzmm C1qtnf5 Capn11 Sema3c H2-T22 Unc5c Sytl4 Galnt5 Sytl2 Arhgap11a Pcdha1 Cdh20 Slc35f2 Trim29 B3gnt5 Dock5 Trim9 Padi4 Pcdh19 Abi2 Cldn11 Slitrk1 Fam13a Nrgn Cpa4 Clmp Il1rap Trpm1 Fat4 Nexn Pmel Mmp15 Fat3 H2-M5 Prss38 Wdr41 Prtg Mlana Mettl22 Tnrc6b Cdh6 Sema3b Ptgfrn Cldn1 Cntn4 Bcl2a1b Capn6 Capn5 Pcdhb19 Tcf15 Bmf Rgs8 Tecrl Tyrp1 Rhot1 Rnf123 Cldn6 Adam9 Hlx Rilpl1 Disp1 Atcay Vwc2 Fat2 Srpx2 Cldn3 Unc13c Creb3l1 Rab39b Robo3 Gpnmb Bves Orai2 Slc22a2 Prss8 Cdh10 Scg3 Adam33 Nyx Dchs1 Chmp4c Syt9 Ap1m2 Megf10 Cthrc1 Penk Igsf9b Akap2 Ltbp3 Dnmbp Tff2 Pnoc Vldlr Cpa3 Snx18 Capn3 Btla Htr1b Gm17231 Pcdh9Rab27a Grm8 Cnih2 Scube2 Id2 Reep1 Cpeb3 Mmp16 Slc18b1 Snx33 Clcn5 Cckbr Pkp2 Drp2 Mapk8ip1 Lrrc3b Cxcl14 Zfhx3 Esrp1 Prx Dock3 Sec14l1 Prokr1 Pstpip2 Usp2 Cpvl Syn2 Ntn1 Ptger1 Rxfp3 Tyr Snap91 Htr1d Mtnr1a Gadd45g Mlph Drd4 Foxc2 Cldn4 Birc7 Cdh17 Twist2 Scnn1b Abcc4 Pkp1 Dlk2 Rab3b Amph Mreg Il33 Slit2 Hpse Micu1 Creb3l2 Dsp Lifr S1pr5 Krt15 Svep1 Ahnak Kcnh1 Sphk1 Vwce Clcf1 Ptch2 Pmp22 Sfrp1 Sema6a Lfng Hs3st5 Efcab1 Tlr5 Muc5acKalrn Vwa2 Fzd8 Lpar6 Bmp5 Slc16a9 Cacng4 Arvcf Igfbp2 Mrvi1 Dusp15 Krt5 Atp13a5 Dsg1a Kcnj14 Edn3Memo1 Ngef Prickle2 Cma1 Alx4 Bmp3 Blnk GastAgtr2 -
Supplementary Figure S4
18DCIS 18IDC Supplementary FigureS4 22DCIS 22IDC C D B A E (0.77) (0.78) 16DCIS 14DCIS 28DCIS 16IDC 28IDC (0.43) (0.49) 0 ADAMTS12 (p.E1469K) 14IDC ERBB2, LASP1,CDK12( CCNE1 ( NUTM2B SDHC,FCGR2B,PBX1,TPR( CD1D, B4GALT3, BCL9, FLG,NUP21OL,TPM3,TDRD10,RIT1,LMNA,PRCC,NTRK1 0 ADAMTS16 (p.E67K) (0.67) (0.89) (0.54) 0 ARHGEF38 (p.P179Hfs*29) 0 ATG9B (p.P823S) (0.68) (1.0) ARID5B, CCDC6 CCNE1, TSHZ3,CEP89 CREB3L2,TRIM24 BRAF, EGFR (7p11); 0 ABRACL (p.R35H) 0 CATSPER1 (p.P152H) 0 ADAMTS18 (p.Y799C) 19q12 0 CCDC88C (p.X1371_splice) (0) 0 ADRA1A (p.P327L) (10q22.3) 0 CCNF (p.D637N) −4 −2 −4 −2 0 AKAP4 (p.G454A) 0 CDYL (p.Y353Lfs*5) −4 −2 Log2 Ratio Log2 Ratio −4 −2 Log2 Ratio Log2 Ratio 0 2 4 0 2 4 0 ARID2 (p.R1068H) 0 COL27A1 (p.G646E) 0 2 4 0 2 4 2 EDRF1 (p.E521K) 0 ARPP21 (p.P791L) ) 0 DDX11 (p.E78K) 2 GPR101, p.A174V 0 ARPP21 (p.P791T) 0 DMGDH (p.W606C) 5 ANP32B, p.G237S 16IDC (Ploidy:2.01) 16DCIS (Ploidy:2.02) 14IDC (Ploidy:2.01) 14DCIS (Ploidy:2.9) -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 Log Ratio Log Ratio Log Ratio Log Ratio 12DCIS 0 ASPM (p.S222T) Log Ratio Log Ratio 0 FMN2 (p.G941A) 20 1 2 3 2 0 1 2 3 2 ERBB3 (p.D297Y) 2 0 1 2 3 20 1 2 3 0 ATRX (p.L1276I) 20 1 2 3 2 0 1 2 3 0 GALNT18 (p.F92L) 2 MAPK4, p.H147Y 0 GALNTL6 (p.E236K) 5 C11orf1, p.Y53C (10q21.2); 0 ATRX (p.R1401W) PIK3CA, p.H1047R 28IDC (Ploidy:2.0) 28DCIS (Ploidy:2.0) 22IDC (Ploidy:3.7) 22DCIS (Ploidy:4.1) 18IDC (Ploidy:3.9) 18DCIS (Ploidy:2.3) 17q12 0 HCFC1 (p.S2025C) 2 LCMT1 (p.S34A) 0 ATXN7L2 (p.X453_splice) SPEN, p.P677Lfs*13 CBFB 1 2 3 4 5 6 7 8 9 10 11