Transcriptomic Uniqueness and Commonality of the Ion Channels and Transporters in the Four Heart Chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Pathways and Networks Biological Meaning of the Gene Sets
Pathways and Networks Biological meaning of the gene sets Gene ontology terms ? Pathway mapping Linking to Pubmed abstracts or associted MESH terms Regulation by the same transcription factor (module) Protein families and domains Gene set enrichment analysis Over representation analysis 1 Gene set enrichment analysis 1. Given an a priori defined set of genes S. 2. Rank genes (e.g. by t‐value between 2 groups of microarray samples) ranked gene list L. 3. Calculation of an enrichment score (ES) that reflects the degree to which a set S is overrepresented at the extremes (top or bottom) of the entire ranked list L. 4. Estimation the statistical significance (nominal P value) of the ES by using an empirical phenotype‐based permutation test procedure. 5. Adjustment for multiple hypothesis testing by controlling the false discovery rate (FDR). Gene set enrichment analysis Subramanian A et al. Proc Natl Acad Sci (2005) 2 Biochemical and Metabolic Pathways Böhringer Mannheim Signaling networks (in cancer cell) Hannah, Weinberg, Cell. 2000 137 NCI curated pathway maps (http://pid.nci.nih.gov) Signal Transduction Knowledge Environment http://stke.sciencemag.org/cm/ 3 Pathways • Pathways are available mechanistic information •Kyoto Encylopedia of Genes and Genomes, KEGG (http://www.genome.jp/kegg/) BioCYc • EcoCYC Metabolic Pathway Database (E. Coli) •Also for other organisms (e.g. HumanCYC) 4 Pathways • Pathways from Biocarta • http://www.biocarta.com/genes/index.asp Transpath Part of larger BioBase package (commercial) • PathwayBuilder package -
The Mineralocorticoid Receptor Leads to Increased Expression of EGFR
www.nature.com/scientificreports OPEN The mineralocorticoid receptor leads to increased expression of EGFR and T‑type calcium channels that support HL‑1 cell hypertrophy Katharina Stroedecke1,2, Sandra Meinel1,2, Fritz Markwardt1, Udo Kloeckner1, Nicole Straetz1, Katja Quarch1, Barbara Schreier1, Michael Kopf1, Michael Gekle1 & Claudia Grossmann1* The EGF receptor (EGFR) has been extensively studied in tumor biology and recently a role in cardiovascular pathophysiology was suggested. The mineralocorticoid receptor (MR) is an important efector of the renin–angiotensin–aldosterone‑system and elicits pathophysiological efects in the cardiovascular system; however, the underlying molecular mechanisms are unclear. Our aim was to investigate the importance of EGFR for MR‑mediated cardiovascular pathophysiology because MR is known to induce EGFR expression. We identifed a SNP within the EGFR promoter that modulates MR‑induced EGFR expression. In RNA‑sequencing and qPCR experiments in heart tissue of EGFR KO and WT mice, changes in EGFR abundance led to diferential expression of cardiac ion channels, especially of the T‑type calcium channel CACNA1H. Accordingly, CACNA1H expression was increased in WT mice after in vivo MR activation by aldosterone but not in respective EGFR KO mice. Aldosterone‑ and EGF‑responsiveness of CACNA1H expression was confrmed in HL‑1 cells by Western blot and by measuring peak current density of T‑type calcium channels. Aldosterone‑induced CACNA1H protein expression could be abrogated by the EGFR inhibitor AG1478. Furthermore, inhibition of T‑type calcium channels with mibefradil or ML218 reduced diameter, volume and BNP levels in HL‑1 cells. In conclusion the MR regulates EGFR and CACNA1H expression, which has an efect on HL‑1 cell diameter, and the extent of this regulation seems to depend on the SNP‑216 (G/T) genotype. -
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. -
Web-Link Name Reference DRSC Flockhart I, Booker M, Kiger A, Et Al.: Flyrnai: the Drosophila Rnai Screening Center Database
web-link Name Reference http://flyrnai.org/cgi-bin/RNAi_screens.pl DRSC Flockhart I, Booker M, Kiger A, et al.: FlyRNAi: the Drosophila RNAi screening center database. Nucleic Acids Res. 34(Database issue):D489-94 (2006) http://flybase.bio.indiana.edu/ FlyBase Grumbling G, Strelets V.: FlyBase: anatomical data, images and queries. Nucleic Acids Res. 34(Database issue):D484-8 (2006) http://rnai.org/ RNAiDB Gunsalus KC, Yueh WC, MacMenamin P, et al.: RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res. 32(Database issue):D406-10 (2004) http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIMOMIM Hamosh A, Scott AF, Amberger JS, et al.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Database issue):D514-7 (2005) http://www.phenomicdb.de/ PhenomicDB Kahraman A, Avramov A, Nashev LG, et al.: PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics. Bioinformatics 21(3):418-20 (2005) http://www.worm.mpi-cbg.de/phenobank2/cgi-bin/MenuPage.pyPhenoBank http://www.informatics.jax.org/ MGI - Mouse Genome Informatics Eppig JT, Bult CJ, Kadin JA, et al.: The Mouse Genome Database (MGD): from genes to mice--a community resource for mouse biology. Nucleic Acids Res. 33(Database issue):D471-5 (2005) http://flight.licr.org/ FLIGHT Sims D, Bursteinas B, Gao Q, et al.: FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets. Nucleic Acids Res. 34(Database issue):D479-83 (2006) http://www.wormbase.org/ WormBase Schwarz EM, Antoshechkin I, Bastiani C, et al.: WormBase: better software, richer content. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
S100A10 in Cancer Progression and Chemotherapy Resistance: a Novel Therapeutic Target Against Ovarian Cancer
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 15 October 2018 doi:10.20944/preprints201810.0318.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 4122; doi:10.3390/ijms19124122 Review S100A10 in Cancer Progression and Chemotherapy Resistance: A Novel Therapeutic Target against Ovarian Cancer Tannith M Noye1, Noor A Lokman1 Martin K Oehler1, 2 and Carmela Ricciardelli1,* 1 Discipline of Obstetrics and Gynaecology, Adelaide Medical School, Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia emails: [email protected] (T.M.N.); [email protected] (N.A.L.); [email protected] (M.K.O); [email protected](C.R) 2 Department of Gynaecological Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia * Correspondence: [email protected]; Tel.: +61-0883138255 Abstract: S100A10, which is also known as p11 is located in the plasma membrane and forms a heterotetramer with annexin A2. The heterotetramer, comprising of 2 subunits of annexin A2 and S100A10, activates the plasminogen activation pathway which is involved in cellular repair of normal tissues. Increased expression of annexin A2 and S100A10 in cancer cells leads to increased levels of plasmin which promote degradation of the extracellular matrix, increased angiogenesis and invasion of the surrounding organs. Although many studies have investigated the functional role of annexin A2 in cancer cells including ovarian cancer, S100A10 has been less studied. We recently demonstrated that high stromal annexin A2 and high cytoplasmic S100A10 expression is associated with a 3.4 fold increased risk of progression and 7.9 fold risk of death in ovarian cancer patients. -
Prox1regulates the Subtype-Specific Development of Caudal Ganglionic
The Journal of Neuroscience, September 16, 2015 • 35(37):12869–12889 • 12869 Development/Plasticity/Repair Prox1 Regulates the Subtype-Specific Development of Caudal Ganglionic Eminence-Derived GABAergic Cortical Interneurons X Goichi Miyoshi,1 Allison Young,1 Timothy Petros,1 Theofanis Karayannis,1 Melissa McKenzie Chang,1 Alfonso Lavado,2 Tomohiko Iwano,3 Miho Nakajima,4 Hiroki Taniguchi,5 Z. Josh Huang,5 XNathaniel Heintz,4 Guillermo Oliver,2 Fumio Matsuzaki,3 Robert P. Machold,1 and Gord Fishell1 1Department of Neuroscience and Physiology, NYU Neuroscience Institute, Smilow Research Center, New York University School of Medicine, New York, New York 10016, 2Department of Genetics & Tumor Cell Biology, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, 3Laboratory for Cell Asymmetry, RIKEN Center for Developmental Biology, Kobe 650-0047, Japan, 4Laboratory of Molecular Biology, Howard Hughes Medical Institute, GENSAT Project, The Rockefeller University, New York, New York 10065, and 5Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724 Neurogliaform (RELNϩ) and bipolar (VIPϩ) GABAergic interneurons of the mammalian cerebral cortex provide critical inhibition locally within the superficial layers. While these subtypes are known to originate from the embryonic caudal ganglionic eminence (CGE), the specific genetic programs that direct their positioning, maturation, and integration into the cortical network have not been eluci- dated. Here, we report that in mice expression of the transcription factor Prox1 is selectively maintained in postmitotic CGE-derived cortical interneuron precursors and that loss of Prox1 impairs the integration of these cells into superficial layers. Moreover, Prox1 differentially regulates the postnatal maturation of each specific subtype originating from the CGE (RELN, Calb2/VIP, and VIP). -
A SARS-Cov-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing Supplementary Information Supplementary Discussion All SARS-CoV-2 protein and gene functions described in the subnetwork appendices, including the text below and the text found in the individual bait subnetworks, are based on the functions of homologous genes from other coronavirus species. These are mainly from SARS-CoV and MERS-CoV, but when available and applicable other related viruses were used to provide insight into function. The SARS-CoV-2 proteins and genes listed here were designed and researched based on the gene alignments provided by Chan et. al. 1 2020 . Though we are reasonably sure the genes here are well annotated, we want to note that not every protein has been verified to be expressed or functional during SARS-CoV-2 infections, either in vitro or in vivo. In an effort to be as comprehensive and transparent as possible, we are reporting the sub-networks of these functionally unverified proteins along with the other SARS-CoV-2 proteins. In such cases, we have made notes within the text below, and on the corresponding subnetwork figures, and would advise that more caution be taken when examining these proteins and their molecular interactions. Due to practical limits in our sample preparation and data collection process, we were unable to generate data for proteins corresponding to Nsp3, Orf7b, and Nsp16. Therefore these three genes have been left out of the following literature review of the SARS-CoV-2 proteins and the protein-protein interactions (PPIs) identified in this study. -
A New Model for X-Linked Tremor/Ataxia
© 2016. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2016) 9, 553-562 doi:10.1242/dmm.022848 RESEARCH ARTICLE Spontaneous shaker rat mutant – a new model for X-linked tremor/ ataxia Karla P. Figueroa1, Sharan Paul1, Tito Calì2, Raffaele Lopreiato2, Sukanya Karan1, Martina Frizzarin2, Darren Ames3, Ginevra Zanni4, Marisa Brini5, Warunee Dansithong1, Brett Milash3, Daniel R. Scoles1, Ernesto Carafoli6 and Stefan M. Pulst1,* ABSTRACT mode of inheritance. Here, we describe the genetic analysis of the The shaker rat is an X-linked recessive spontaneous model of shaker rat, a model of Purkinje cell (PC) degeneration. This mutant progressive Purkinje cell (PC) degeneration exhibiting a shaking arose spontaneously and was observed in Sprague Dawley (SD) ataxia and wide stance. Generation of Wistar Furth (WF)/Brown outbred stock in 1991 at Saint Louis University, first described by Norwegian (BN) F1 hybrids and genetic mapping of F2 sib-sib La Regina et al. (1992), and the phenotype of whole-body tremor, ‘ ’ offspring using polymorphic markers narrowed the candidate gene ataxia and wide stance designated as shaker . The shaker trait was region to 26 Mbp denoted by the last recombinant genetic marker reported as an X-linked recessive trait. DXRat21 at 133 Mbp to qter (the end of the long arm). In the WF Various animal models of spontaneously occurring mutants that background, the shaker mutation has complete penetrance, results in parallel some aspects of human hereditary ataxia have been a stereotypic phenotype and there is a narrow window for age of reported; for example, weaver, lurcher, stumbler, tottering and disease onset; by contrast, the F2 hybrid phenotype was more varied, teetering mice (Chou et al., 1991; Frankel et al., 1994; Green and with a later age of onset and likely non-penetrance of the mutation. -
Somatic and Inherited Mutations in Primary Aldosteronism
59 1 F L FERNANDES-ROSA, S BOULKROUN Genetic basis of 59: 1 R47–R63 Review and others primary aldosteronism Somatic and inherited mutations in primary aldosteronism Fabio Luiz Fernandes-Rosa1,2,3,*, Sheerazed Boulkroun1,2,* and Maria-Christina Zennaro1,2,3 Correspondence should be addressed 1 INSERM, UMRS_970, Paris Cardiovascular Research Center, Paris, France to F L Fernandes-Rosa or 2 University Paris Descartes, Sorbonne Paris Cité, Paris, France S Boulkroun 3 Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Email Paris, France fabio.fernandes-rosa@ *(F L Fernandes-Rosa and S Boulkroun contributed equally to this work) inserm.fr or sheerazed. [email protected] Abstract Primary aldosteronism (PA), the most common form of secondary hypertension, is caused Key Words in the majority of cases by unilateral aldosterone-producing adenoma (APA) or bilateral f primary aldosteronism adrenal hyperplasia. Over the past few years, somatic mutations in KCNJ5, CACNA1D, f aldosterone-producing ATP1A1 and ATP2B3 have been proven to be associated with APA development, adenoma representing more than 50% of sporadic APA. The identification of these mutations has f familial allowed the development of a model for APA involving modification on the intracellular hyperaldosteronism ionic equilibrium and regulation of cell membrane potential, leading to autonomous f somatic mutations aldosterone overproduction. Furthermore, somatic CTNNB1 mutations have also been f germline mutations identified in APA, but the link between these mutations and APA development remains f potassium channels unknown. The sequence of events responsible for APA formation is not completely f calcium channels understood, in particular, whether a single hit or a double hit is responsible for both f ATPase Journal of Molecular Endocrinology aldosterone overproduction and cell proliferation. -
Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al.