Literature DB >> 28257835

Stratifying melanoma and breast cancer TCGA datasets on the basis of the CNV of transcription factor binding sites common to proliferation- and apoptosis-effector genes.

James A Mauro1, John M Yavorski1, George Blanck2.   

Abstract

Transcription factors that activate both proliferation- and apoptosis-effector genes, along with a number of related observations, have led to a proposal for a feed forward mechanism of activating the two gene classes, whereby a certain concentration of a transcription factor activates the proliferation-effector genes and a higher concentration of the transcription factor activates the apoptosis-effector genes. We reasoned that this paradigm of regulation could lead to, in the cancer setting, a selection for relatively reduced copy numbers of apoptosis-effector gene, transcription factor binding sites (TFBS). Thus, the aim of this investigation was to examine the DNA sequencing read depths of TFBS for a set of proliferation- and apoptosis-effector genes, normalized to the read depths found in matching blood samples, as provided by the cancer genome atlas (TCGA); and thereby document copy number differences among these TFBS. We determined that the melanoma and breast cancer, TCGA datasets could be divided into three categories: (i) no detectable copy number variation for the proliferation- and apoptosis-effector, shared TFBS; (ii) a relative increase in the copy number of proliferation-effector gene TFBS, compared with the copy number of the apoptosis-effector gene TFBS; and (iii) a relative decrease in the number of proliferation-effector gene TFBS. Thus, we conclude that changes in the relative copies of the shared TFBS, for proliferation- and apoptosis-effector genes, have the potential of impacting tumor cell proliferative and apoptotic capacities.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Apoptosis-effector genes; Cancer; Copy number variation; Feed-forward mechanism; Proliferation-effector genes; Transcription factor binding sites

Mesh:

Substances:

Year:  2017        PMID: 28257835     DOI: 10.1016/j.gene.2017.02.026

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  5 in total

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Authors:  Dhiraj Sikaria; Yaping N Tu; Diana A Fisler; James A Mauro; George Blanck
Journal:  J Cancer Res Clin Oncol       Date:  2018-01-05       Impact factor: 4.553

2.  Elucidating feed-forward apoptosis signatures in breast cancer datasets: Higher FOS expression associated with a better outcome.

Authors:  Diana A Fisler; Dhiraj Sikaria; John M Yavorski; Yaping N Tu; George Blanck
Journal:  Oncol Lett       Date:  2018-06-12       Impact factor: 2.967

3.  Screening and identification of potential biomarkers and therapeutic drugs in melanoma via integrated bioinformatics analysis.

Authors:  Bo Chen; Donghong Sun; Xiuni Qin; Xing-Hua Gao
Journal:  Invest New Drugs       Date:  2021-01-26       Impact factor: 3.850

4.  An age-based, RNA expression paradigm for survival biomarker identification for pediatric neuroblastoma and acute lymphoblastic leukemia.

Authors:  Andrea Diviney; Boris I Chobrutskiy; Saif Zaman; George Blanck
Journal:  Cancer Cell Int       Date:  2019-03-27       Impact factor: 5.722

5.  High-Resolution Copy Number Patterns From Clinically Relevant FFPE Material.

Authors:  Anastasia Filia; Alastair Droop; Mark Harland; Helene Thygesen; Juliette Randerson-Moor; Helen Snowden; Claire Taylor; Joey Mark S Diaz; Joanna Pozniak; Jérémie Nsengimana; Jon Laye; Julia A Newton-Bishop; D Timothy Bishop
Journal:  Sci Rep       Date:  2019-06-20       Impact factor: 4.379

  5 in total

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