Literature DB >> 18453086

Clinical uses of microarrays in cancer research.

Carl Virtanen1, James Woodgett.   

Abstract

Perturbations in genes play a key role in the pathogenesis of cancer. Microarray-based technology is an ideal way in which to study the effects and interactions of multiple genes in cancer. There are many technologic challenges in running a microarray study, including annotation of genes likely to be involved, designing the appropriate experiment, and ensuring adequate quality assurance steps are implemented. Once data are normalized, they need to be analyzed; and for this, there are numerous software packages and approaches.

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Mesh:

Year:  2008        PMID: 18453086      PMCID: PMC4485473          DOI: 10.1007/978-1-60327-148-6_6

Source DB:  PubMed          Journal:  Methods Mol Med        ISSN: 1543-1894


  76 in total

1.  Validating clustering for gene expression data.

Authors:  K Y Yeung; D R Haynor; W L Ruzzo
Journal:  Bioinformatics       Date:  2001-04       Impact factor: 6.937

2.  BLAT--the BLAST-like alignment tool.

Authors:  W James Kent
Journal:  Genome Res       Date:  2002-04       Impact factor: 9.043

3.  Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes.

Authors:  Jennifer Pittman; Erich Huang; Holly Dressman; Cheng-Fang Horng; Skye H Cheng; Mei-Hua Tsou; Chii-Ming Chen; Andrea Bild; Edwin S Iversen; Andrew T Huang; Joseph R Nevins; Mike West
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-19       Impact factor: 11.205

Review 4.  From signatures to models: understanding cancer using microarrays.

Authors:  Eran Segal; Nir Friedman; Naftali Kaminski; Aviv Regev; Daphne Koller
Journal:  Nat Genet       Date:  2005-06       Impact factor: 38.330

5.  Multiplexed biochemical assays with biological chips.

Authors:  S P Fodor; R P Rava; X C Huang; A C Pease; C P Holmes; C L Adams
Journal:  Nature       Date:  1993-08-05       Impact factor: 49.962

6.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

7.  Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer.

Authors:  Gennadi V Glinsky; Olga Berezovska; Anna B Glinskii
Journal:  J Clin Invest       Date:  2005-06       Impact factor: 14.808

8.  Cluster analysis and display of genome-wide expression patterns.

Authors:  M B Eisen; P T Spellman; P O Brown; D Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-12-08       Impact factor: 11.205

9.  Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor.

Authors:  James R Vasselli; Joanna H Shih; Shuba R Iyengar; Jodi Maranchie; Joseph Riss; Robert Worrell; Carlos Torres-Cabala; Ray Tabios; Andra Mariotti; Robert Stearman; Maria Merino; McClellan M Walther; Richard Simon; Richard D Klausner; W Marston Linehan
Journal:  Proc Natl Acad Sci U S A       Date:  2003-05-30       Impact factor: 11.205

10.  Gene expression profiling identifies clinically relevant subtypes of prostate cancer.

Authors:  Jacques Lapointe; Chunde Li; John P Higgins; Matt van de Rijn; Eric Bair; Kelli Montgomery; Michelle Ferrari; Lars Egevad; Walter Rayford; Ulf Bergerheim; Peter Ekman; Angelo M DeMarzo; Robert Tibshirani; David Botstein; Patrick O Brown; James D Brooks; Jonathan R Pollack
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-07       Impact factor: 11.205

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  9 in total

1.  Microarray analysis of cutaneous squamous cell carcinomas reveals enhanced expression of epidermal differentiation complex genes.

Authors:  Laurie G Hudson; James M Gale; R Steven Padilla; Gavin Pickett; Bryan E Alexander; Jing Wang; Donna F Kusewitt
Journal:  Mol Carcinog       Date:  2010-07       Impact factor: 4.784

Review 2.  Targeting stem cells-clinical implications for cancer therapy.

Authors:  Lan Chun Tu; Greg Foltz; Edward Lin; Leroy Hood; Qiang Tian
Journal:  Curr Stem Cell Res Ther       Date:  2009-05       Impact factor: 3.828

3.  Gene expression profiles can predict panitumumab monotherapy responsiveness in human tumor xenograft models.

Authors:  Michael J Boedigheimer; Daniel J Freeman; Panteha Kiaei; Michael A Damore; Robert Radinsky
Journal:  Neoplasia       Date:  2013-02       Impact factor: 5.715

4.  Novel molecular imaging platform for monitoring oncological kinases.

Authors:  Shyam Nyati; Brian D Ross; Alnawaz Rehemtulla; Mahaveer S Bhojani
Journal:  Cancer Cell Int       Date:  2010-07-08       Impact factor: 5.722

5.  GPX2 and BMP4 as Significant Molecular Alterations in The Lung Adenocarcinoma Progression: Integrated Bioinformatics Analysis.

Authors:  Mohammad Hossein Derakhshan Nazari; Rana Askari Dastjerdi; Parnian Ghaedi Talkhouncheh; Ahmad Bereimipour; Hamidreza Mollasalehi; Amir Ali Mahshad; Ali Razi; Mohammad Hossein Nazari; Amin Ebrahimi Sadrabadi; Sara Taleahmad
Journal:  Cell J       Date:  2022-06-29       Impact factor: 3.128

6.  TAaCGH Suite for Detecting Cancer-Specific Copy Number Changes Using Topological Signatures.

Authors:  Jai Aslam; Sergio Ardanza-Trevijano; Jingwei Xiong; Javier Arsuaga; Radmila Sazdanovic
Journal:  Entropy (Basel)       Date:  2022-06-29       Impact factor: 2.738

Review 7.  From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma.

Authors:  Androu Arsanious; Georg A Bjarnason; George M Yousef
Journal:  Mol Cancer       Date:  2009-03-17       Impact factor: 27.401

Review 8.  Bioinformatics for cancer immunology and immunotherapy.

Authors:  Pornpimol Charoentong; Mihaela Angelova; Mirjana Efremova; Ralf Gallasch; Hubert Hackl; Jerome Galon; Zlatko Trajanoski
Journal:  Cancer Immunol Immunother       Date:  2012-09-18       Impact factor: 6.968

9.  A robust and accurate method for feature selection and prioritization from multi-class OMICs data.

Authors:  Vittorio Fortino; Pia Kinaret; Nanna Fyhrquist; Harri Alenius; Dario Greco
Journal:  PLoS One       Date:  2014-09-23       Impact factor: 3.240

  9 in total

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