Literature DB >> 15665294

Wavelet transformations of tumor expression profiles reveals a pervasive genome-wide imprinting of aneuploidy on the cancer transcriptome.

Amit Aggarwal1, Siew Hong Leong, Cheryl Lee, Oi Lian Kon, Patrick Tan.   

Abstract

Aneuploidy is frequently observed in many human cancers, but its global effects on the cancer transcriptome are controversial. We did a systematic and unbiased genome-wide survey to determine the extent a tumor's abnormal karyotype (chromosomal amplifications and deletions) is detectably "imprinted" onto that tumor's gene expression profile. By using a novel methodology employing wavelet transform signal-processing algorithms to identify genomic regions of coordinated gene expression (wavelet variance scanning), we analyzed a series of gastric cancer cell lines and identified >100 genomic regions exhibiting distinct patterns of subtle but significant coordinated transcription, ranging from tens to hundreds of genes. A large majority (80%) of these regions could be specifically localized to a site of detectable genomic amplification or deletion; reciprocally, up to 47% of the total aneuploidy in each of the individual cell lines could be directly inferred from the gene expression data. Genome-wide portraits of tumor aneuploidy can thus be successfully reconstructed solely from gene expression data, implying that the effects of aneuploidy must be pervasively and globally imprinted within the cancer transcriptome. Aneuploidy may contribute to tumor behavior not just by affecting the expression of a few key oncogenes and tumor suppressor genes but also by subtly altering the expression levels of hundreds of genes in the oncogenome.

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Year:  2005        PMID: 15665294

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  5 in total

1.  Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays.

Authors:  Robert B Scharpf; Giovanni Parmigiani; Jonathan Pevsner; Ingo Ruczinski
Journal:  Ann Appl Stat       Date:  2008-06-01       Impact factor: 2.083

2.  Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data.

Authors:  Guanghua Xiao; Xinlei Wang; Arkady B Khodursky
Journal:  J Am Stat Assoc       Date:  2011-03       Impact factor: 5.033

3.  The gene-reduction effect of chromosomal losses detected in gastric cancers.

Authors:  Seung-Jin Hong; Eun-Jung Jeon; Jung-Hwan Oh; Eun-Joo Seo; Sang-Wook Choi; Mun-Gan Rhyu
Journal:  BMC Gastroenterol       Date:  2010-11-20       Impact factor: 3.067

Review 4.  Identifying driver genes in cancer by triangulating gene expression, gene location, and survival data.

Authors:  Sigrid Rouam; Lance D Miller; R Krishna Murthy Karuturi
Journal:  Cancer Inform       Date:  2015-02-03

5.  Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas.

Authors:  Federico E Turkheimer; Federico Roncaroli; Benoit Hennuy; Christian Herens; Minh Nguyen; Didier Martin; Annick Evrard; Vincent Bours; Jacques Boniver; Manuel Deprez
Journal:  BMC Bioinformatics       Date:  2006-12-01       Impact factor: 3.169

  5 in total

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