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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Immuno-Oncology Panel 1
Immuno-Oncology panel 1 Gene Symbol Target protein name UniProt ID (& link) Modification* (56 analytes) ADA17 ADAM17 metalloprotease domain 17 P78536 *blanks mean the assay detects the ANXA1 Annexin A1 P04083 non-modified peptide sequence ANXA1 Annexin A1 P04083 ARG2 arginase, type II P78540 ATM Serine-protein kinase ATM, Ataxia telangiectasia mutated Q13315 pS2996 ATM Serine-protein kinase ATM, Ataxia telangiectasia mutated Q13315 ATM Serine-protein kinase ATM, Ataxia telangiectasia mutated Q13315 pS367 ATM Serine-protein kinase ATM, Ataxia telangiectasia mutated Q13315 C10orf54 / VISTA chromosome 10 open reading frame 54 Q9H7M9 CCL5 C-C motif chemokine ligand 5 P13501 CD14 CD14 molecule P08571 CD163 CD163 molecule Q86VB7 CD274 / PDL1 Programmed cell death 1 ligand 1 CD274 Q9NZQ7 CD33 CD33 molecule P20138 CD40/TNR5 tumor necrosis factor receptor superfamily member 5 P25942 CD40/TNR5 tumor necrosis factor receptor superfamily member 5 P25942 CD47 CD47 molecule Q08722 CD70 CD70 antigen P32970 CD74/HG2A CD74 molecule, major histocompatibility complex, class II invariant chain Q8SNA0 CEACAM8 carcinoembryonic antigen-related cell adhesion molecule 8 P31997 CX3CL1 C-X3-C motif chemokine ligand 1 P78423 CXCL10 C-X-C motif chemokine ligand 10 P02778 CXCL13 chemokine (C-X-C motif) ligand 13 O43927 ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 Q86VV3 FAS/TNR6 Fas (TNF receptor superfamily, member 6) P25445 pY291 FAS/TNR6 Fas (TNF receptor superfamily, member 6) P25445 GAPDH Glyceraldehyde-3-phosphate dehydrogenase P04406 HAVCR2 hepatitis -
Functional T Cells Phosphodiesterase 7A-Deficient Mice Have
Phosphodiesterase 7A-Deficient Mice Have Functional T Cells Guchen Yang, Kim W. McIntyre, Robert M. Townsend, Henry H. Shen, William J. Pitts, John H. Dodd, Steven G. This information is current as Nadler, Murray McKinnon and Andrew J. Watson of October 2, 2021. J Immunol 2003; 171:6414-6420; ; doi: 10.4049/jimmunol.171.12.6414 http://www.jimmunol.org/content/171/12/6414 Downloaded from References This article cites 37 articles, 17 of which you can access for free at: http://www.jimmunol.org/content/171/12/6414.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average by guest on October 2, 2021 Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2003 by The American Association of Immunologists All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Phosphodiesterase 7A-Deficient Mice Have Functional T Cells Guchen Yang,1 Kim W. McIntyre, Robert M. Townsend, Henry H. Shen, William J. Pitts, John H. Dodd, Steven G. Nadler, Murray McKinnon, and Andrew J. -
Regulation of Cdc42 and Its Effectors in Epithelial Morphogenesis Franck Pichaud1,2,*, Rhian F
© 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs217869. doi:10.1242/jcs.217869 REVIEW SUBJECT COLLECTION: ADHESION Regulation of Cdc42 and its effectors in epithelial morphogenesis Franck Pichaud1,2,*, Rhian F. Walther1 and Francisca Nunes de Almeida1 ABSTRACT An overview of Cdc42 Cdc42 – a member of the small Rho GTPase family – regulates cell Cdc42 was discovered in yeast and belongs to a large family of small – polarity across organisms from yeast to humans. It is an essential (20 30 kDa) GTP-binding proteins (Adams et al., 1990; Johnson regulator of polarized morphogenesis in epithelial cells, through and Pringle, 1990). It is part of the Ras-homologous Rho subfamily coordination of apical membrane morphogenesis, lumen formation and of GTPases, of which there are 20 members in humans, including junction maturation. In parallel, work in yeast and Caenorhabditis elegans the RhoA and Rac GTPases, (Hall, 2012). Rho, Rac and Cdc42 has provided important clues as to how this molecular switch can homologues are found in all eukaryotes, except for plants, which do generate and regulate polarity through localized activation or inhibition, not have a clear homologue for Cdc42. Together, the function of and cytoskeleton regulation. Recent studies have revealed how Rho GTPases influences most, if not all, cellular processes. important and complex these regulations can be during epithelial In the early 1990s, seminal work from Alan Hall and his morphogenesis. This complexity is mirrored by the fact that Cdc42 can collaborators identified Rho, Rac and Cdc42 as main regulators of exert its function through many effector proteins. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.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 © 2016 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 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Inhibiting PDE7A Enhances the Protective Effects of Neural Stem
Research Article: New Research | Cognition and Behavior Inhibiting PDE7A enhances the protective effects of neural stem cells on neurodegeneration and memory deficits in sevoflurane-exposed mice https://doi.org/10.1523/ENEURO.0071-21.2021 Cite as: eNeuro 2021; 10.1523/ENEURO.0071-21.2021 Received: 19 February 2021 Revised: 21 May 2021 Accepted: 25 May 2021 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.eneuro.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2021 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 Inhibiting PDE7A enhances the protective effects of neural stem cells on 2 neurodegeneration and memory deficits in sevoflurane-exposed mice 3 Yanfang Huang, Yingle Chen*, Zhenming Kang, Shunyuan Li* 4 Department of Anesthesiology, Quanzhou First Hospital Affiliated to Fujian Medical 5 University, Quanzhou 362000, Fujian, China 6 7 *Corresponding authors 8 Shunyuan Li and Yingle Chen 9 Department of Anesthesiology, Quanzhou First Hospital Affiliated to Fujian Medical 10 University, Quanzhou 362000, Fujian, China 11 Email: [email protected] (Shunyuan Li); [email protected] (Yingle Chen) 12 Tel: 86-18960333666 13 14 15 Running title: Role of PDE7A in neurodegeneration 16 17 1 18 Abstract 19 Sevoflurane is widely used in general anesthesia, especially for children. -
Conformational Disruption of Pi3kδ Regulation by Immunodeficiency Mutations in PIK3CD and PIK3R1
Conformational disruption of PI3Kδ regulation by immunodeficiency mutations in PIK3CD and PIK3R1 Gillian L. Dornana, Braden D. Siempelkampa, Meredith L. Jenkinsa, Oscar Vadasb, Carrie L. Lucasc, and John E. Burkea,1 aDepartment of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada V8W 2Y2; bDepartment of Pharmaceutical Chemistry, University of Geneva, CH-1211 Geneva 4, Switzerland; and cDepartment of Immunobiology, Yale University, New Haven, CT 06511 Edited by Lewis C. Cantley, Weill Cornell Medical College, New York, NY, and approved January 12, 2017 (received for review November 2, 2016) Activated PI3K Delta Syndrome (APDS) is a primary immunodefi- domain, and a bilobal kinase domain. All p85 regulatory subunits ciency disease caused by activating mutations in either the contain two SH2 domains (nSH2 and cSH2) linked by a coiled-coil leukocyte-restricted p110δ catalytic (PIK3CD) subunit or the ubiq- region referred to as the inter-SH2 domain (iSH2). The class IA uitously expressed p85α regulatory (PIK3R1) subunit of class IA p110 catalytic subunits are differentially inhibited by p85, with phosphoinositide 3-kinases (PI3Ks). There are two classes of APDS: p110α containing inhibitory contacts between the nSH2 domain of α APDS1 that arises from p110δ mutations that are analogous to p85 and the C2, helical, and kinase domains of p110 , as well as α oncogenic mutations found in the broadly expressed p110α sub- between the iSH2 domain of p85 and the C2 domain of p110 (10). Both p110β and p110δ contain an additional regulatory unit and APDS2 that occurs from a splice mutation resulting in – p85α with a central deletion (Δ434–475). -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Transcriptomic Analysis of the Aquaporin (AQP) Gene Family
Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples. -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
The Atypical Guanine-Nucleotide Exchange Factor, Dock7, Negatively Regulates Schwann Cell Differentiation and Myelination
The Journal of Neuroscience, August 31, 2011 • 31(35):12579–12592 • 12579 Cellular/Molecular The Atypical Guanine-Nucleotide Exchange Factor, Dock7, Negatively Regulates Schwann Cell Differentiation and Myelination Junji Yamauchi,1,3,5 Yuki Miyamoto,1 Hajime Hamasaki,1,3 Atsushi Sanbe,1 Shinji Kusakawa,1 Akane Nakamura,2 Hideki Tsumura,2 Masahiro Maeda,4 Noriko Nemoto,6 Katsumasa Kawahara,5 Tomohiro Torii,1 and Akito Tanoue1 1Department of Pharmacology and 2Laboratory Animal Resource Facility, National Research Institute for Child Health and Development, Setagaya, Tokyo 157-8535, Japan, 3Department of Biological Sciences, Tokyo Institute of Technology, Midori, Yokohama 226-8501, Japan, 4IBL, Ltd., Fujioka, Gumma 375-0005, Japan, and 5Department of Physiology and 6Bioimaging Research Center, Kitasato University School of Medicine, Sagamihara, Kanagawa 252-0374, Japan In development of the peripheral nervous system, Schwann cells proliferate, migrate, and ultimately differentiate to form myelin sheath. In all of the myelination stages, Schwann cells continuously undergo morphological changes; however, little is known about their underlying molecular mechanisms. We previously cloned the dock7 gene encoding the atypical Rho family guanine-nucleotide exchange factor (GEF) and reported the positive role of Dock7, the target Rho GTPases Rac/Cdc42, and the downstream c-Jun N-terminal kinase in Schwann cell migration (Yamauchi et al., 2008). We investigated the role of Dock7 in Schwann cell differentiation and myelination. Knockdown of Dock7 by the specific small interfering (si)RNA in primary Schwann cells promotes dibutyryl cAMP-induced morpholog- ical differentiation, indicating the negative role of Dock7 in Schwann cell differentiation. It also results in a shorter duration of activation of Rac/Cdc42 and JNK, which is the negative regulator of myelination, and the earlier activation of Rho and Rho-kinase, which is the positive regulator of myelination. -
PI3K Catalytic Isoform Alteration Promotes the LIMK1-Related
ANTICANCER RESEARCH 37 : 1805-1818 (2017) doi:10.21873/anticanres.11515 PI3K Catalytic Isoform Alteration Promotes the LIMK1-related Metastasis Through the PAK1 or ROCK1/2 Activation in Cigarette Smoke-exposed Ovarian Cancer Cells GA BIN PARK 1 and DAEJIN KIM 2 1Department of Biochemistry, Kosin University College of Medicine, Busan, Republic of Korea; 2Department of Anatomy, Inje University College of Medicine, Busan, Republic of Korea Abstract. Aim: To investigate the molecular mechanisms Several studies have shown a strong correlation between by which long-term exposure to cigarette smoke extract cigarette smoke (CS) and cancer metastasis through the (CSE) contributes to ovarian cancer metastasis. Materials induction of numerous factors involved in migration activity and Methods: Western blot analysis for diverse p110 (1-3). The exposure to CS induces the epithelial- isoforms of phosphoinositide 3-kinase (PI3K)-related mesenchymal transition (EMT) process and up-regulates the signaling pathway and epithelial-mesenchymal transition expression of EMT markers, including N-cadherin and (EMT) markers was performed to analyze the underlying vimentin (4, 5). Cigarette smoke extract (CSE) treatment mechanisms. Migratory activity of CSE-exposed ovarian significantly induces interleukin-8 (IL-8) and transforming cancer cells was determined by transendothelial migration growth factor-beta 1 (TGF- β1 ) production and profoundly and invasion assay. Results: After exposure to CSE for four suppresses the proliferation and growth of erythroid and weeks, CaOV3 (primary) and SKOV3 (metastatic) ovarian granulocyte-macrophage progenitors (6). Stimulation with cancer cells showed enhanced mesenchymal characteristics CSE in human lung fibroblast cells induces the expression and produced EMT-related cytokines [intwerleukin-8 (IL-8), of phosphorylated Smad3, a main downstream target of the vascular endothelial growth factor (VEGF) and TGF- β1 receptor, which results in the secretion of vascular transforming growth factor-beta 1 (TGF- β1 )].