TSHZ3 Deletion Causes an Autism Syndrome and Defects in Cortical Projection Neurons
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Novel Tumor Subgroups of Urothelial Carcinoma of the Bladder Defined by Integrated Genomic Analysis
Published OnlineFirst August 29, 2012; DOI: 10.1158/1078-0432.CCR-12-1807 Clinical Cancer Human Cancer Biology Research Novel Tumor Subgroups of Urothelial Carcinoma of the Bladder Defined by Integrated Genomic Analysis Carolyn D. Hurst, Fiona M. Platt, Claire F. Taylor, and Margaret A. Knowles Abstract Purpose: There is a need for improved subclassification of urothelial carcinoma (UC) at diagnosis. A major aim of this study was to search for novel genomic subgroups. Experimental design: We assessed 160 tumors for genome-wide copy number alterations and mutation in genes implicated in UC. These comprised all tumor grades and stages and included 49 high-grade stage T1 (T1G3) tumors. Results: Our findings point to the existence of genomic subclasses of the "gold-standard" grade/stage groups. The T1G3 tumors separated into 3 major subgroups that differed with respect to the type and number of copy number events and to FGFR3 and TP53 mutation status. We also identified novel regions of copy number alteration, uncovered relationships between molecular events, and elucidated relationships between molecular events and clinico-pathologic features. FGFR3 mutant tumors were more chromosom- ally stable than their wild-type counterparts and a mutually exclusive relationship between FGFR3 mutation and overrepresentation of 8q was observed in non-muscle-invasive tumors. In muscle-invasive (MI) tumors, metastasis was positively associated with losses of regions on 10q (including PTEN), 16q and 22q, and gains on 10p, 11q, 12p, 19p, and 19q. Concomitant copy number alterations positively associated with TP53 mutation in MI tumors were losses on 16p, 2q, 4q, 11p, 10q, 13q, 14q, 16q, and 19p, and gains on 1p, 8q, 10q, and 12q. -
Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea. -
University of Florida Thesis Or Dissertation Formatting Template
STATISTICAL METHODS FOR ANALYZING GENOMICS DATA By SINJINI SIKDAR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017 © 2017 Sinjini Sikdar Dedicated to my husband, Sandipan, who has been a constant source of support and encouragement during the challenges of my PhD life. ACKNOWLEDGMENTS I am deeply grateful to my advisor Professor Susmita Datta for her unyielding support and invaluable guidance throughout my graduate work. It would not have been possible for me to reach this point without her support. I also want to express my sincere thanks to Professor Somnath Datta as I learnt a lot through my interactions with him and benefited from his expert suggestions. I am also thankful to my other dissertation committee members Professor Fei Zou, and Professor Lauren McIntyre for all their kind support and constructive comments regarding my dissertation. I want to thank Professor Ryan Gill from University of Louisville for his helpful contributions to my research works. I also want to thank all the faculty, staff, and students of the Department of Biostatistics at University of Florida as well as all the faculty and students of the Department of Bioinformatics and Biostatistics at University of Louisville who have helped me reach this point. I would like to thank my sister Shreejata Sikdar for her invaluable friendship and constant support throughout my PhD life. Finally, I want to thank my parents for their immense encouragement that have helped me in moving forward in my academic life. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
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 -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Microrna-93 May Control Vascular Endothelial Growth Factor a in Circulating Peripheral Blood Mononuclear Cells in Acute Kawasaki Disease
nature publishing group Translational Investigation Articles MicroRNA-93 may control vascular endothelial growth factor A in circulating peripheral blood mononuclear cells in acute Kawasaki disease Kazuyoshi Saito1, Hideyuki Nakaoka1, Ichiro Takasaki2, Keiichi Hirono1, Seiji Yamamoto3, Koshi Kinoshita4, Nariaki Miyao1, Keijiro Ibuki1, Sayaka Ozawa1, Kazuhiro Watanabe1, Neil E. Bowles5 and Fukiko Ichida1 BACKGROUND: Kawasaki disease (KD) is the most common (4–8), and several signaling pathways, including transforming systemic vasculitis syndrome primarily affecting medium-sized growth factor-β (TGF-β), Nuclear factor of kappa B (NF-κB), arteries, particularly the coronary arteries. Though KD may be and VEGF, have been reported to be involved in immune dys- associated with immunological problems, the involvement of regulation and the pathogenesis of acute KD (2,9). VEGF-A is microRNAs (miRs) has not been fully described. well known to have a role in angiogenesis, in vasodilation by METHODS: We enrolled 23 KD patients and 12 controls. We indirect nitric oxide release, and in the chemotactic function of performed miR and mRNA microarray analysis of peripheral macrophages and granulocytes (10,11). We previously reported blood mononuclear cells (PBMCs) isolated from acute KD that serum levels of VEGF-A correlate with the development of patients and controls. Continuously, we measured specific coronary arterial lesions (CAL) in KD patients (3). miRs, mRNA and the expression of proteins by using reverse- MicroRNA (miRs) are small noncoding RNAs, 18–25 nucle- transcriptase PCR (RT-PCR) and enzyme-linked immunosor- otides in length, that mediate gene silencing through imperfect bent assay (ELISA). hybridization to 3’ untranslated regions in target mRNAs and RESULTS: We identified strikingly high levels of miR-182 and modulate a variety of biological activities including immunolog- miR-296-5p during the acute febrile phase, and of miR-93, miR- ical reactions. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
RAMP1 and RAMP3 Differentially Control Amylin's Effects on Food
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2020 RAMP1 and RAMP3 differentially control amylin’s effects on food intake, glucose and energy balance in male and female mice Coester, Bernd Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-191827 Dissertation Published Version Originally published at: Coester, Bernd. RAMP1 and RAMP3 differentially control amylin’s effects on food intake, glucose and energy balance in male and female mice. 2020, University of Zurich, Vetsuisse Faculty. Institut für Veterinärphysiologie der Vetsuisse-Fakultät Universität Zürich Direktor: Prof. Prof. h.c. Dr. med. vet. Max Gassmann Arbeit unter wissenschaftlicher Betreuung von Christelle Le Foll, PhD RAMP1 and RAMP3 Differentially Control Amylin’s Effects on Food Intake, Glucose and Energy Balance in Male and Female Mice Inaugural-Dissertation zur Erlangung der Doktorwürde der Vetsuisse-Fakultät Universität Zürich vorgelegt von Bernd Coester Tierarzt von Zürich, ZH genehmigt auf Antrag von Prof. Dr. med. vet. Thomas Lutz, Referent 2020 Inhaltsverzeichnis Zusammenfassung 4 Abstract 5 Introduction 6 Experimental Procedures 7 Results 9 Discussion 19 References 24 Appendix 26 3 RAMP1 und RAMP3 kontrollieren die Effekte von Amylin auf Futteraufnahme, Glukose und Energiehaushalt in männlichen und weiblichen Mäusen Bernd Coester, Sydney W Pence, Soraya Arrigoni, Christina N Boyle, Christelle Le Foll, Thomas A Lutz Amylin ist ein Peptid aus dem endokrinen Pankreas und nimmt eine Schlüsselrolle in der Kontrolle von Futteraufnahme und Energiehaushalt ein, wobei es mehrheitlich an drei Rezeptoren bindet (AMY 1-3). AMY 1-3 bestehen aus einem Calcitonin- Rezeptor (CTR) und jeweils einem rezeptor-aktivität-modifizierenden Protein (RAMP1-3). -
Downloaded from Refseq Database ( Duct Development (After E7.5), in Which Both Ducts Re
Roly et al. BMC Genomics (2020) 21:688 https://doi.org/10.1186/s12864-020-07106-8 RESEARCH ARTICLE Open Access Transcriptional landscape of the embryonic chicken Müllerian duct Zahida Yesmin Roly, Rasoul Godini, Martin A. Estermann, Andrew T. Major, Roger Pocock and Craig A. Smith* Abstract Background: Müllerian ducts are paired embryonic tubes that give rise to the female reproductive tract in vertebrates. Many disorders of female reproduction can be attributed to anomalies of Müllerian duct development. However, the molecular genetics of Müllerian duct formation is poorly understood and most disorders of duct development have unknown etiology. In this study, we describe for the first time the transcriptional landscape of the embryonic Müllerian duct, using the chicken embryo as a model system. RNA sequencing was conducted at 1 day intervals during duct formation to identify developmentally-regulated genes, validated by in situ hybridization. Results: This analysis detected hundreds of genes specifically up-regulated during duct morphogenesis. Gene ontology and pathway analysis revealed enrichment for developmental pathways associated with cell adhesion, cell migration and proliferation, ERK and WNT signaling, and, interestingly, axonal guidance. The latter included factors linked to neuronal cell migration or axonal outgrowth, such as Ephrin B2, netrin receptor, SLIT1 and class A semaphorins. A number of transcriptional modules were identified that centred around key hub genes specifying matrix-associated signaling factors; SPOCK1, HTRA3 and ADGRD1. Several novel regulators of the WNT and TFG-β signaling pathway were identified in Müllerian ducts, including APCDD1 and DKK1, BMP3 and TGFBI.A number of novel transcription factors were also identified, including OSR1, FOXE1, PRICKLE1, TSHZ3 and SMARCA2. -
CGRP Signaling Via CALCRL Increases Chemotherapy Resistance and Stem Cell Properties in Acute Myeloid Leukemia
International Journal of Molecular Sciences Article CGRP Signaling via CALCRL Increases Chemotherapy Resistance and Stem Cell Properties in Acute Myeloid Leukemia 1,2 1,2, 1,2, 1,2 Tobias Gluexam , Alexander M. Grandits y, Angela Schlerka y, Chi Huu Nguyen , Julia Etzler 1,2 , Thomas Finkes 1,2, Michael Fuchs 3, Christoph Scheid 3, Gerwin Heller 1,2 , Hubert Hackl 4 , Nathalie Harrer 5, Heinz Sill 6 , Elisabeth Koller 7 , Dagmar Stoiber 8,9, Wolfgang Sommergruber 10 and Rotraud Wieser 1,2,* 1 Division of Oncology, Department of Medicine I, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; [email protected] (T.G.); [email protected] (A.M.G.); [email protected] (A.S.); [email protected] (C.H.N.); [email protected] (J.E.); thomas.fi[email protected] (T.F.); [email protected] (G.H.) 2 Comprehensive Cancer Center, Spitalgasse 23, 1090 Vienna, Austria 3 Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany; [email protected] (M.F.); [email protected] (C.S.) 4 Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80, 6020 Innsbruck, Austria; [email protected] 5 Department for Cancer Research, Boehringer Ingelheim RCV GmbH & Co KG, Dr. Boehringer-Gasse 5-11, A-1121 Vienna, Austria; [email protected] 6 Division of Hematology, Medical University of Graz, Auenbruggerplatz -
Table SII. Significantly Differentially Expressed Mrnas of GSE23558 Data Series with the Criteria of Adjusted P<0.05 And
Table SII. Significantly differentially expressed mRNAs of GSE23558 data series with the criteria of adjusted P<0.05 and logFC>1.5. Probe ID Adjusted P-value logFC Gene symbol Gene title A_23_P157793 1.52x10-5 6.91 CA9 carbonic anhydrase 9 A_23_P161698 1.14x10-4 5.86 MMP3 matrix metallopeptidase 3 A_23_P25150 1.49x10-9 5.67 HOXC9 homeobox C9 A_23_P13094 3.26x10-4 5.56 MMP10 matrix metallopeptidase 10 A_23_P48570 2.36x10-5 5.48 DHRS2 dehydrogenase A_23_P125278 3.03x10-3 5.40 CXCL11 C-X-C motif chemokine ligand 11 A_23_P321501 1.63x10-5 5.38 DHRS2 dehydrogenase A_23_P431388 2.27x10-6 5.33 SPOCD1 SPOC domain containing 1 A_24_P20607 5.13x10-4 5.32 CXCL11 C-X-C motif chemokine ligand 11 A_24_P11061 3.70x10-3 5.30 CSAG1 chondrosarcoma associated gene 1 A_23_P87700 1.03x10-4 5.25 MFAP5 microfibrillar associated protein 5 A_23_P150979 1.81x10-2 5.25 MUCL1 mucin like 1 A_23_P1691 2.71x10-8 5.12 MMP1 matrix metallopeptidase 1 A_23_P350005 2.53x10-4 5.12 TRIML2 tripartite motif family like 2 A_24_P303091 1.23x10-3 4.99 CXCL10 C-X-C motif chemokine ligand 10 A_24_P923612 1.60x10-5 4.95 PTHLH parathyroid hormone like hormone A_23_P7313 6.03x10-5 4.94 SPP1 secreted phosphoprotein 1 A_23_P122924 2.45x10-8 4.93 INHBA inhibin A subunit A_32_P155460 6.56x10-3 4.91 PICSAR P38 inhibited cutaneous squamous cell carcinoma associated lincRNA A_24_P686965 8.75x10-7 4.82 SH2D5 SH2 domain containing 5 A_23_P105475 7.74x10-3 4.70 SLCO1B3 solute carrier organic anion transporter family member 1B3 A_24_P85099 4.82x10-5 4.67 HMGA2 high mobility group AT-hook 2 A_24_P101651