Segmentation of genomic and transcriptomic microarrays data reveals major correlation between DNA copy number aberrations and gene–loci expression

Ortiz-Estévez, María; Rivas, Javier De Las; Fontanillo, Celia; Rubio, Ángel

doi:10.1016/j.ygeno.2010.10.008

Cited by 23 publications

(20 citation statements)

References 40 publications

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“…Interestingly, recent evidence suggests that these pathways are deregulated in multiple sporadic cancers (Beroukhim et al , 2010). Iodine-131-related changes in tumour tissue may result not only from epigenetic changes but also from copy number alterations (Kang et al , 2006), shown to shape cancerous transcriptome (Ortiz-Estevez et al , 2011). This idea finds some support in that AUTS2 gene located within 7q11.22–7q11.23 region, previously found to be amplified in post-Chernobyl tumours (13), showed a suggestive dose dependency in the tumour tissue based on 2 d.f.…”

Section: Discussionmentioning

confidence: 99%

Iodine-131 dose-dependent gene expression: alterations in both normal and tumour thyroid tissues of post-Chernobyl thyroid cancers

et al. 2013

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Background:A strong, consistent association between childhood irradiation and subsequent thyroid cancer provides an excellent model for studying radiation carcinogenesis.Methods:We evaluated gene expression in 63 paired RNA specimens from frozen normal and tumour thyroid tissues with individual iodine-131 (I-131) doses (0.008–8.6 Gy, no unirradiated controls) received from Chernobyl fallout during childhood (Ukrainian-American cohort). Approximately half of these randomly selected samples (32 tumour/normal tissue RNA specimens) were hybridised on 64 whole-genome microarrays (Agilent, 4 × 44 K). Associations between I-131 dose and gene expression were assessed separately in normal and tumour tissues using Kruskal−Wallis and linear trend tests. Of 155 genes significantly associated with I-131 after Bonferroni correction and with ⩾2-fold increase per dose category, we selected 95 genes. On the remaining 31 RNA samples these genes were used for validation purposes using qRT−PCR.Results:Expression of eight genes (ABCC3, C1orf9, C6orf62, FGFR1OP2, HEY2, NDOR1, STAT3, and UCP3) in normal tissue and six genes (ANKRD46, CD47, HNRNPH1, NDOR1, SCEL, and SERPINA1) in tumour tissue was significantly associated with I-131. PANTHER/DAVID pathway analyses demonstrated significant over-representation of genes coding for nucleic acid binding in normal and tumour tissues, and for p53, EGF, and FGF signalling pathways in tumour tissue.Conclusion:The multistep process of radiation carcinogenesis begins in histologically normal thyroid tissue and may involve dose-dependent gene expression changes.

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Section: Discussionmentioning

confidence: 99%

Iodine-131 dose-dependent gene expression: alterations in both normal and tumour thyroid tissues of post-Chernobyl thyroid cancers

et al. 2013

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“…As a distinctive element from other integrative approaches we do not consider only SNPs or genes individually. We take into account the gene loci following the strategy described in [11], that is based on the application of the same smoothing and segmentation algorithm to CN and GE in order to establish comparable regions. Once we get the smoothed segments, we perform two independent analyses for each gene loci: a signal correlation analysis and an alteration frequency analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Our method takes into account the genomic loci, both in the copy number (CN) analysis and also in the gene expression (GE) analysis, and applies the segmentation step proposed by Ortiz-Estevez et al . [11]. These authors designed a method for robust comparison between CN and GE using paired samples.…”

Section: Introductionmentioning

confidence: 99%

Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer

et al. 2012

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BackgroundAnalysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations.ResultsFollowing this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes. We applied our method to a human dataset of 64 Glioblastoma Multiforme samples finding key loci and hotspots that correspond to major alterations previously described for this type of tumors.ConclusionsIdentification of key altered genomic loci constitutes a first step to find the genes that drive the alteration in a malignant state. These driver genes can be found in regions that show high correlation in copy number alterations and expression changes.

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“…The matched transcriptomics data is then examined to see if a gene's altered copy number is associated with a concurrent change in the gene's expression [2]–[17], thus adding weight to the argument that the gene may be contributing to the type of cancer in question [18]. A number of algorithms and bioinformatics tools have been published to aid this type of study [17], [19]–[23]. Matched data sets have also been used for cancer subtype stratification [21], [24]–[26].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

Newton

Wernisch

2014

PLoS ONE

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Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.

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Segmentation of genomic and transcriptomic microarrays data reveals major correlation between DNA copy number aberrations and gene–loci expression

Cited by 23 publications

References 40 publications

Iodine-131 dose-dependent gene expression: alterations in both normal and tumour thyroid tissues of post-Chernobyl thyroid cancers

Iodine-131 dose-dependent gene expression: alterations in both normal and tumour thyroid tissues of post-Chernobyl thyroid cancers

Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer

A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

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