Literature DB >> 26113463

Microarray Analysis in Glioblastomas.

Kaumudi M Bhawe1, Manish K Aghi2.   

Abstract

Microarray analysis in glioblastomas is done using either cell lines or patient samples as starting material. A survey of the current literature points to transcript-based microarrays and immunohistochemistry (IHC)-based tissue microarrays as being the preferred methods of choice in cancers of neurological origin. Microarray analysis may be carried out for various purposes including the following: i. To correlate gene expression signatures of glioblastoma cell lines or tumors with response to chemotherapy (DeLay et al., Clin Cancer Res 18(10):2930-2942, 2012). ii. To correlate gene expression patterns with biological features like proliferation or invasiveness of the glioblastoma cells (Jiang et al., PLoS One 8(6):e66008, 2013). iii. To discover new tumor classificatory systems based on gene expression signature, and to correlate therapeutic response and prognosis with these signatures (Huse et al., Annu Rev Med 64(1):59-70, 2013; Verhaak et al., Cancer Cell 17(1):98-110, 2010). While investigators can sometimes use archived tumor gene expression data available from repositories such as the NCBI Gene Expression Omnibus to answer their questions, new arrays must often be run to adequately answer specific questions. Here, we provide a detailed description of microarray methodologies, how to select the appropriate methodology for a given question, and analytical strategies that can be used. Experimental methodology for protein microarrays is outside the scope of this chapter, but basic sample preparation techniques for transcript-based microarrays are included here.

Entities:  

Keywords:  Gene-expression; Glioblastoma; Microarray

Mesh:

Substances:

Year:  2016        PMID: 26113463      PMCID: PMC5056625          DOI: 10.1007/7651_2015_245

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  20 in total

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Review 2.  Protein microarrays for diagnostic assays.

Authors:  Michael Hartmann; Johan Roeraade; Dieter Stoll; Markus F Templin; Thomas O Joos
Journal:  Anal Bioanal Chem       Date:  2008-09-20       Impact factor: 4.142

Review 3.  Current progress for the use of miRNAs in glioblastoma treatment.

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Journal:  Mol Neurobiol       Date:  2013-04-28       Impact factor: 5.590

4.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

5.  AltAnalyze and DomainGraph: analyzing and visualizing exon expression data.

Authors:  Dorothea Emig; Nathan Salomonis; Jan Baumbach; Thomas Lengauer; Bruce R Conklin; Mario Albrecht
Journal:  Nucleic Acids Res       Date:  2010-05-31       Impact factor: 16.971

6.  Global regulation of alternative splicing by adenosine deaminase acting on RNA (ADAR).

Authors:  Oz Solomon; Shirley Oren; Michal Safran; Naamit Deshet-Unger; Pinchas Akiva; Jasmine Jacob-Hirsch; Karen Cesarkas; Reut Kabesa; Ninette Amariglio; Ron Unger; Gideon Rechavi; Eran Eyal
Journal:  RNA       Date:  2013-03-08       Impact factor: 4.942

7.  Glioblastoma: molecular analysis and clinical implications.

Authors:  Jason T Huse; Eric Holland; Lisa M DeAngelis
Journal:  Annu Rev Med       Date:  2012-10-01       Impact factor: 13.739

8.  Synergistic interactions between camptothecin and EGFR or RAC1 inhibitors and between imatinib and Notch signaling or RAC1 inhibitors in glioblastoma cell lines.

Authors:  Linda Sooman; Simon Ekman; Claes Andersson; Hanna Göransson Kultima; Anders Isaksson; Fredrik Johansson; Michael Bergqvist; Erik Blomquist; Johan Lennartsson; Joachim Gullbo
Journal:  Cancer Chemother Pharmacol       Date:  2013-06-05       Impact factor: 3.333

9.  NFAT1 is highly expressed in, and regulates the invasion of, glioblastoma multiforme cells.

Authors:  Xinxin Tie; Sheng Han; Lingxuan Meng; Yunjie Wang; Anhua Wu
Journal:  PLoS One       Date:  2013-06-06       Impact factor: 3.240

10.  Caveolin-1 is a negative regulator of tumor growth in glioblastoma and modulates chemosensitivity to temozolomide.

Authors:  Kevin Quann; Donna M Gonzales; Isabelle Mercier; Chenguang Wang; Federica Sotgia; Richard G Pestell; Michael P Lisanti; Jean-François Jasmin
Journal:  Cell Cycle       Date:  2013-04-17       Impact factor: 4.534

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  2 in total

Review 1.  Cell-based immunotherapy of glioblastoma multiforme.

Authors:  Igor Bryukhovetskiy
Journal:  Oncol Lett       Date:  2022-02-23       Impact factor: 2.967

2.  Analysis of Gene Expression Profiles in the Liver of Rats With Intrauterine Growth Retardation.

Authors:  Zheng Shen; Weifen Zhu; Lizhong Du
Journal:  Front Pediatr       Date:  2022-03-07       Impact factor: 3.418

  2 in total

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