Cyclin G (CCNG1) Rabbit Polyclonal Antibody – TA347367

OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA347367 Cyclin G (CCNG1) Rabbit Polyclonal Antibody Product data: Product Type: Primary Antibodies Applications: WB Recommended Dilution: WB: 1:500-1:3000 Reactivity: Human, Mouse, Rat Host: Rabbit Isotype: IgG Clonality: Polyclonal Immunogen: The immunogen for anti-Cyclin G Antibody: A synthesized peptide derived from human Cyclin G Formulation: Rabbit IgG in phosphate buffered saline , pH 7.4, 150mM NaCl, 0.02% sodium azide and 50% glycerol. Store at -20?. Stable for 12 months from date of receipt Concentration: lot specific Purification: Affinity-chromatography Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 29 kDa Gene Name: cyclin G1 Database Link: NP_954854 Entrez Gene 12450 MouseEntrez Gene 25405 RatEntrez Gene 900 Human P51959 Background: CCNG1 May play a role in growth regulation. Is associated with G2/M phase arrest in response to DNA damage. May be an intermediate by which p53 mediates its role as an inhibitor of cellular proliferation. Belongs to the cyclin family. Cyclin G subfamily. Activated in breast and prostate cancer cells. Activated by actinomycin-D induced DNA damage. Synonyms: CCNG Note: Cyclin G antibody detects endogenous levels of total Cyclin G This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 Cyclin G (CCNG1) Rabbit Polyclonal Antibody – TA347367 Protein Families: Druggable Genome Protein Pathways: p53 signaling pathway Product images: Western blot analysis on Jurkat cell lysate using Cyclin G Antibody This product is to be used for laboratory only. Not for diagnostic or therapeutic use. ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 2 / 2.

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Chromosomal Aberrations in Head and Neck Squamous Cell Carcinomas in Norwegian and Sudanese Populations by Array Comparative Genomic Hybridization
825-843 12/9/08 15:31 Page 825 ONCOLOGY REPORTS 20: 825-843, 2008 825 Chromosomal aberrations in head and neck squamous cell carcinomas in Norwegian and Sudanese populations by array comparative genomic hybridization ERIC ROMAN1,2, LEONARDO A. MEZA-ZEPEDA3, STINE H. KRESSE3, OLA MYKLEBOST3,4, ENDRE N. VASSTRAND2 and SALAH O. IBRAHIM1,2 1Department of Biomedicine, Faculty of Medicine and Dentistry, University of Bergen, Jonas Lies vei 91; 2Department of Oral Sciences - Periodontology, Faculty of Medicine and Dentistry, University of Bergen, Årstadveien 17, 5009 Bergen; 3Department of Tumor Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center, Montebello, 0310 Oslo; 4Department of Molecular Biosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway Received January 30, 2008; Accepted April 29, 2008 DOI: 10.3892/or_00000080 Abstract. We used microarray-based comparative genomic logical parameters showed little correlation, suggesting an hybridization to explore genome-wide profiles of chromosomal occurrence of gains/losses regardless of ethnic differences and aberrations in 26 samples of head and neck cancers compared clinicopathological status between the patients from the two to their pair-wise normal controls. The samples were obtained countries. Our findings indicate the existence of common from Sudanese (n=11) and Norwegian (n=15) patients. The gene-specific amplifications/deletions in these tumors, findings were correlated with clinicopathological variables. regardless of the source of the samples or attributed We identified the amplification of 41 common chromosomal carcinogenic risk factors. regions (harboring 149 candidate genes) and the deletion of 22 (28 candidate genes). Predominant chromosomal alterations Introduction that were observed included high-level amplification at 1q21 (harboring the S100A gene family) and 11q22 (including Head and neck squamous cell carcinoma (HNSCC), including several MMP family members).
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Genome-Wide Approach to Identify Risk Factors for Therapy-Related Myeloid Leukemia
Leukemia (2006) 20, 239–246 & 2006 Nature Publishing Group All rights reserved 0887-6924/06 $30.00 www.nature.com/leu ORIGINAL ARTICLE Genome-wide approach to identify risk factors for therapy-related myeloid leukemia A Bogni1, C Cheng2, W Liu2, W Yang1, J Pfeffer1, S Mukatira3, D French1, JR Downing4, C-H Pui4,5,6 and MV Relling1,6 1Department of Pharmaceutical Sciences, The University of Tennessee, Memphis, TN, USA; 2Department of Biostatistics, The University of Tennessee, Memphis, TN, USA; 3Hartwell Center, The University of Tennessee, Memphis, TN, USA; 4Department of Pathology, The University of Tennessee, Memphis, TN, USA; 5Department of Hematology/Oncology St Jude Children’s Research Hospital, The University of Tennessee, Memphis, TN, USA; and 6Colleges of Medicine and Pharmacy, The University of Tennessee, Memphis, TN, USA Using a target gene approach, only a few host genetic risk therapy increases, the importance of identifying host factors for factors for treatment-related myeloid leukemia (t-ML) have been secondary neoplasms increases. defined. Gene expression microarrays allow for a more 4 genome-wide approach to assess possible genetic risk factors Because DNA microarrays interrogate multiple ( 10 000) for t-ML. We assessed gene expression profiles (n ¼ 12 625 genes in one experiment, they allow for a ‘genome-wide’ probe sets) in diagnostic acute lymphoblastic leukemic cells assessment of genes that may predispose to leukemogenesis. from 228 children treated on protocols that included leukemo- DNA microarray analysis of gene expression has been used to genic agents such as etoposide, 13 of whom developed t-ML. identify distinct expression profiles that are characteristic of Expression of 68 probes, corresponding to 63 genes, was different leukemia subtypes.13,14 Studies using this method have significantly related to risk of t-ML.
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IGH Rearrangement in Myeloid Neoplasms Sion and Activation
CASE REPORTS genes such as MYC and BCL2, leading to their overexpres- IGH rearrangement in myeloid neoplasms sion and activation. Here, we found two IGH rearrange- ments in myeloid tumors, including an IGH-MECOM in a Though immunoglobulin genes are typically expressed myelodysplastic syndrome (MDS) and an IGH-CCNG1 in in B lymphocytes, recent studies found ectopic an AML. Our studies provide the first evidence that a once immunoglobulin expression in non-B-cell tumor cells believed B-cell tumor-specific oncogenic mechanism is including acute myeloid leukemia (AML). The also present in myeloid tumors. immunoglobulin genes, including immunoglobulin heavy Case #1: A 60-year-old female with a history of breast chain genes (IGH), light kappa (k) chain genes (IGK) and cancer presented with fatigue, bilateral flank pain, and 9 light lambda (λ) chain genes (IGL), are frequently hematuria. Complete blood count showed 24.82x10 /L of rearranged in B-cell tumors. These rearrangements result white blood cells, 11.2 g/dL of hemoglobin, 33.9% of in a juxtaposition of IG enhancers to the vicinity of onco- hematocrit, and 68x109/L of platelets. The bone marrow A B C D E Figure 1. Acute myeloid leukemia with an IGH-CCNG1 rearrangement. (A) Chromosome analysis of the bone marrow aspirate showed a complex karyotype, including an unbalanced translocation involving chromosomes 5, 14 and 19. Arrows indicate clonal aberrations. (B) Fluorescence in situ hybridization (FISH) on abnormal metaphase showed the 3'IGH (red) remaining on the derivative chromosome 14 and the 5'IGH lost, consistent with an unbalanced IGH rearrange- ment.
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Molecular Phenotyping Using Networks, Diffusion, and Topology
www.nature.com/scientificreports OPEN Molecular phenotyping using networks, difusion, and topology: soft tissue sarcoma Received: 27 November 2018 James C. Mathews 1, Maryam Pouryahya1, Caroline Moosmüller 2, Yannis G. Kevrekidis2, Accepted: 6 September 2019 Joseph O. Deasy1 & Allen Tannenbaum3 Published: xx xx xxxx Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses infuence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of difusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profles of the sarcomas in the Cancer Genome Atlas along the TP53 (p53) signaling network. The signatures substantially recover the leiomyosarcoma, dediferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, and they also include a new signature defned by activation and inactivation of about a dozen genes, including activation of serine endopeptidase inhibitor SERPINE1 and inactivation of TP53-family tumor suppressor gene TP73. Modern biological investigations ofen result in dense, high-dimensional datasets describing genes, proteins, mutations, or other variables. A near universal problem arises as the dimensionality of the data grows: how can the data be investigated in a relatively unbiased manner, to expose underlying clusters and relational structure? To date, there is a lack of robust techniques for exposing the structure of biological data in an unbiased, agnostic fashion.
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Commentary the cyclical behavior of the so-called “canon- Exploiting Oncogenic Drivers ical cyclins” that agreeably and accordingly marked the major phase transitions of the along the CCNG1 Pathway for mammalian cell division cycle. Fortunately, what was hidden from the wise Cancer Therapy and Gene Therapy in terms of rigid canonical considerations was revealed to the experimentalists and Ahmad Al-Shihabi,1 Sant P. Chawla,1 Frederick L. Hall,2 physician-scientists who looked beyond the and Erlinda M. Gordon1,2,3 meager definitions to explore the actual structure and function relations of the cyclin https://doi.org/10.1016/j.omto.2018.11.002 G1 protein (by genetic engineering of CCNG1) in the context of cancer gene ther- The search for strategic molecular targets recognize specific structural features or apy, long before cyclin G1 was determined among a myriad of cell signaling pathways target sequences, characterized by Gordon to be the prime molecular driver of the has long been a cornerstone for both molec- et al.,1 arrayed along major cell-cycle and elusive cell competence factor, the pivotal ex- ular cancer research and gene therapy gene-regulatory proteins. Thus, a canonical ecutive component of the Cyclin G1/p53/ research, leading to the development of novel “cyclin” is thought of as an oscillating, posi- Mdm2 Axis governing cell cycle checkpoint cancer therapies that act at the level of the tive-acting “regulatory subunit” of an identi- control and, perhaps, a most strategic target genome (DNA), the transcriptome (RNA), fied CDK, which both “activates” the for new precision molecular and genetic can- and/or the proteome (protein).
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Viewed and Approved by the Quencing (RRBS) Kit (Diagenode) Following Manufacturer’S Institutional Animal Care and Use Committee of University Instruction
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Open Moore Sarah P53network.Pdf
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Cyclin G (CCNG1) Rabbit Polyclonal Antibody – TA347367 | Origene