Literature DB >> 17265718

Microarrays for cancer diagnosis and classification.

Ainhoa Perez-Diez1, Andrey Morgun, Natalia Shulzhenko.   

Abstract

Microarray analysis has yet to be widely accepted for diagnosis and classification of human cancers, despite the exponential increase in microarray studies reported in the literature. Among several methods available, a few refined approaches have evolved for the analysis of microarray data for cancer diagnosis. These include class comparison, class prediction and class discovery. Using as examples some of the major experimental contributions recently provided in the field of both hematological and solid tumors, we discuss the steps required to utilize microarray data to obtain general and reliable gene profiles that could be universally used in clinical laboratories. As we show, microarray technology is not only a new tool for the clinical lab but it can also improve the accuracy of the classical diagnostic techniques by suggesting novel tumor-specific markers. We then highlight the importance of publicly available microarray data and the development of their integrated analysis that may fulfill the promise that this new technology holds for cancer diagnosis and classification.

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Mesh:

Year:  2007        PMID: 17265718     DOI: 10.1007/978-0-387-39978-2_8

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  17 in total

Review 1.  Diagnostic microarrays in hematologic oncology: applications of high- and low-density arrays.

Authors:  Tatyana V Nasedkina; Natalia A Guseva; Olga A Gra; Olga N Mityaeva; Alexander V Chudinov; Alexander S Zasedatelev
Journal:  Mol Diagn Ther       Date:  2009       Impact factor: 4.074

2.  Accurate and reliable cancer classification based on probabilistic inference of pathway activity.

Authors:  Junjie Su; Byung-Jun Yoon; Edward R Dougherty
Journal:  PLoS One       Date:  2009-12-07       Impact factor: 3.240

3.  Global changes in processing of mRNA 3' untranslated regions characterize clinically distinct cancer subtypes.

Authors:  Priyam Singh; Travis L Alley; Sarah M Wright; Sonya Kamdar; William Schott; Robert Y Wilpan; Kevin D Mills; Joel H Graber
Journal:  Cancer Res       Date:  2009-12-15       Impact factor: 12.701

4.  ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer.

Authors:  Feng-Hsiang Chung; Henry Hsin-Chung Lee; Hoong-Chien Lee
Journal:  PLoS One       Date:  2013-06-14       Impact factor: 3.240

Review 5.  Comprehensive literature review and statistical considerations for microarray meta-analysis.

Authors:  George C Tseng; Debashis Ghosh; Eleanor Feingold
Journal:  Nucleic Acids Res       Date:  2012-01-19       Impact factor: 16.971

Review 6.  Pathological bases for a robust application of cancer molecular classification.

Authors:  Salvador J Diaz-Cano
Journal:  Int J Mol Sci       Date:  2015-04-17       Impact factor: 5.923

7.  A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

Authors:  Ke-Shiuan Lynn; Li-Lan Li; Yen-Ju Lin; Chiuen-Huei Wang; Shu-Hui Sheng; Ju-Hwa Lin; Wayne Liao; Wen-Lian Hsu; Wen-Harn Pan
Journal:  Bioinformatics       Date:  2009-02-23       Impact factor: 6.937

8.  Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

Authors:  David M Mutch; M Ramzi Temanni; Corneliu Henegar; Florence Combes; Véronique Pelloux; Claus Holst; Thorkild I A Sørensen; Arne Astrup; J Alfredo Martinez; Wim H M Saris; Nathalie Viguerie; Dominique Langin; Jean-Daniel Zucker; Karine Clément
Journal:  PLoS One       Date:  2007-12-19       Impact factor: 3.240

9.  Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Authors:  Wei Liu; Xuefeng Bai; Yuejuan Liu; Wei Wang; Junwei Han; Qiuyu Wang; Yanjun Xu; Chunlong Zhang; Shihua Zhang; Xuecang Li; Zhonggui Ren; Jian Zhang; Chunquan Li
Journal:  Sci Rep       Date:  2015-08-19       Impact factor: 4.379

10.  VEZT, a novel putative tumor suppressor, suppresses the growth and tumorigenicity of gastric cancer.

Authors:  Ruizhen Miao; Xiaobo Guo; Qiaoming Zhi; Yulong Shi; Leping Li; Xuehui Mao; Li Zhang; Chensheng Li
Journal:  PLoS One       Date:  2013-09-17       Impact factor: 3.240

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