Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

Bioinformatics and Biostatistics Unit, and Translational Research Laboratory, Catalan Institute of Oncology, IDIBELL, L'Hospitalet, Barcelona, Spain.
BMC Genomics (Impact Factor: 3.99). 02/2008; 9(1):12. DOI: 10.1186/1471-2164-9-12
Source: PubMed


Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally.
This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC.
This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.

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Available from: Xavier Solé,
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    • "It plays an important role in the development of many types of cancer, including BC [17]. Sole and coworkers [18] proposed that variations in putative cis-regulators of transcription in 8q24 can significantly alter germline c-MYC expression levels and thereby contribute to cancer susceptibility. "
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    ABSTRACT: The onset and progression of breast cancer (BC) is influenced by many factors, including the single nucleotide polymorphism (SNP) rs13281615 at 8q24. However, studies of the potential association between rs13281615 at 8q24 and risk of BC have given inconsistent results. We performed a meta-analysis to address this controversy. PubMed, EMBASE and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. Two curators independently extracted data, and odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated to assess the strength of the association between rs13281615 at 8q24 and risk of BC. Fourteen studies are included in the meta-analysis, involving 44,283 cases (5,170 Chinese and 39,113 mixed) and 55,756 controls (5,589 Chinese and 50,167 mixed). The GG and G-allele genotypes of rs13281615 at 8q24 are significantly associated with increased risk of BC (GG vs. AG+AA, OR 1.13, 95% CI 1.08-1.19, P<0.001; G-allele vs. A-allele, OR 1.10, 95% CI 1.06-1.14, P<0.001; GG vs. AA, OR 1.20, 95% CI 1.12-1.29, P<0.001). Conversely, the AA genotype is significantly associated with decreased risk of BC (AA vs. AG+GG, OR 0.89, 95% CI 0.84-0.93, P<0.001). G-allele genotypes of rs13281615 at 8q24 polymorphism are a risk factor for developing BC, while the AA genotype is a protective factor. Further large and well-designed studies are required to confirm this conclusion.
    PLoS ONE 04/2013; 8(4):e60108. DOI:10.1371/journal.pone.0060108 · 3.23 Impact Factor
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    • "MYC is one of the most studied oncogenes stemming from its association with a large number of diseases and indeed, a link to MYC expression for alleles associated with particular SNPs would be a first candidate gene of choice. In concert with this hypothesis, Sole et al. (2008) found consistent up-regulated expression of MYC in normal prostate samples with regard to at least one risk locus (rs1447295). However, another group (Pomerantz et al., 2009a) failed to find any correlation between MYC expression and a risk allele (including rs1447295) in their normal prostate samples. "
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    ABSTRACT: Understanding the functional effects of the wide-range of aberrant genetic characteristics associated with the human chromosome 8q24 region in cancer remains daunting due to the complexity of the locus. The most logical target for study remains the MYC proto-oncogene, a prominent resident of 8q24 that was first identified more than a quarter of a century ago. However, many of the amplifications, translocation breakpoints, and viral integration sites associated with 8q24 are often found throughout regions surrounding large expanses of the MYC locus that include other transcripts. In addition, chr.8q24 is host to a number of single nucleotide polymorphisms associated with cancer risk. Yet, the lack of a direct correlation between cancer risk alleles and MYC expression has also raised the possibility that MYC is not always the target of these genetic associations. The 8q24 region has been described as a "gene desert" because of the paucity of functionally annotated genes located within this region. Here we review the evidence for the role of other loci within the 8q24 region, most of which are non-coding transcripts, either in concert with MYC or independent of MYC, as possible candidate gene targets in malignancy.
    Frontiers in Genetics 04/2012; 3:69. DOI:10.3389/fgene.2012.00069
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    • "ARACNE removes indirect interactions by using the data process inequality (DPI), a property of MI [8,12]. ARACNE has been used to identify putative transcriptional targets of the cancer related genes MYC and KLF6 [13], and to reconstruct breast, colorectal, and glial normal and cancerous tissue gene coexpression networks [14]. "
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    ABSTRACT: The onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses. In this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements. Despite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.
    BMC Genomics 10/2010; 11(1):578. DOI:10.1186/1471-2164-11-578 · 3.99 Impact Factor
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