Dataset Information

CBX7 and HMGA1b proteins act in opposite way on the regulation of the SPP1 gene expression.

ABSTRACT: Several recent studies have reported the Polycomb Repressive Complex 1 member CBX7 as a tumor-suppressor gene whose expression progressively decreases in different human carcinomas in relation with tumor grade, malignant stage and poor prognosis. We have previously demonstrated that CBX7 is able to inhibit the expression of the SPP1 gene, encoding the chemokine osteopontin that is over-expressed in cancer and has a critical role in cancer progression. Here, we have analyzed the mechanism by which CBX7 regulates the SPP1 gene expression. We show that the SPP1 transcriptional regulation mechanism involves the CBX7-interacting protein HMGA1b, that acts as a positive regulator of the SPP1 gene. In fact, we demonstrate that, in contrast with the transcriptional activity of CBX7, HMGA1b is able to increase the SPP1 expression by inducing the activity of its promoter. Moreover, we show that CBX7 interferes with HMGA1b on the SPP1 promoter and counteracts the positive transcriptional activity of HMGA1b on the SPP1 expression. Furthermore, since we found that also the NF-?B complex resulted involved in the modulation of the SPP1 expression in thyroid cells, we suppose that CBX7/HMGA1b/NF-?B could take part in the same transcriptional mechanism that finally leads to the regulation of the SPP1 gene expression. Taken together, our data show the important role played by CBX7 in the negative regulation of the SPP1 gene expression, thus contributing to prevent the acquisition of a malignant phenotype.

SUBMITTER: Sepe R

PROVIDER: S-EPMC4413610 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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CBX7 and HMGA1b proteins act in opposite way on the regulation of the SPP1 gene expression.

Sepe Romina R Formisano Umberto U Federico Antonella A Forzati Floriana F Bastos André Uchimura AU D'Angelo Daniela D Cacciola Nunzio Antonio NA Fusco Alfredo A Pallante Pierlorenzo P

Oncotarget 20150201 5

Several recent studies have reported the Polycomb Repressive Complex 1 member CBX7 as a tumor-suppressor gene whose expression progressively decreases in different human carcinomas in relation with tumor grade, malignant stage and poor prognosis. We have previously demonstrated that CBX7 is able to inhibit the expression of the SPP1 gene, encoding the chemokine osteopontin that is over-expressed in cancer and has a critical role in cancer progression. Here, we have analyzed the mechanism by whic ...[more]

PMID: 25595895

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Project description:BackgroundA family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material.ResultsThe simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively.ConclusionsInitial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Dataset Information

CBX7 and HMGA1b proteins act in opposite way on the regulation of the SPP1 gene expression.

Publications

CBX7 and HMGA1b proteins act in opposite way on the regulation of the SPP1 gene expression.

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