Histone modifications silence the GATA transcription factor genes in ovarian cancer

Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA 19111-2497, USA.
Oncogene (Impact Factor: 8.46). 09/2006; 25(39):5446-61. DOI: 10.1038/sj.onc.1209533
Source: PubMed


Altered expression of GATA factors was found and proposed as the underlying mechanism for dedifferentiation in ovarian carcinogenesis. In particular, GATA6 is lost or excluded from the nucleus in 85% of ovarian tumors and GATA4 expression is absent in majority of ovarian cancer cell lines. Here, we evaluated their DNA and histone epigenetic modifications in five ovarian epithelial and carcinoma cell lines (human 'immortalized' ovarian surface epithelium (HIO)-117, HIO-114, A2780, SKOV3 and ES2). GATA4 and GATA6 gene silencing was found to correlate with hypoacetylation of histones H3 and H4 and loss of histone H3/lysine K4 tri-methylation at their promoters in all lines. Conversely, histone H3/lysine K9 di-methylation and HP1gamma association were not observed, excluding reorganization of GATA genes into heterochromatic structures. The histone deacetylase inhibitor trichostatin A, but not the DNA methylation inhibitor 5'-aza-2'-deoxycytidine, re-established the expression of GATA4 and/or GATA6 in A2780 and HIO-114 cells, correlating with increased histone H3 and H4 acetylation, histone H3 lysine K4 methylation and DNase I sensitivity at the promoters. Therefore, altered histone modification of the promoter loci is one mechanism responsible for the silencing of GATA transcription factors and the subsequent loss of a target gene, the tumor suppressor Disabled-2, in ovarian carcinogenesis.

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    • "Two genes correspond to more than two of the 139 CpGs, both are well-known tumour suppressor genes: DCC (DCC = Deleted in Colorectal Cancer) and GATA4. The GATA4 promoter is hypermethylated in cancer [28] and it is involved in ovarian cancer [29-31]. Interestingly, the two most important human DNA regions of a recent DNAm cervical pre-cancer classifier, EPB41L3 and DPYS [32], are amongst the ~100 regions corresponding to the 139 CpGs. "
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    ABSTRACT: Background DNA methylation (DNAm) has important regulatory roles in many biological processes and diseases. It is the only epigenetic mark with a clear mechanism of mitotic inheritance and the only one easily available on a genome scale. Aberrant cytosine-phosphate-guanine (CpG) methylation has been discussed in the context of disease aetiology, especially cancer. CpG hypermethylation of promoter regions is often associated with silencing of tumour suppressor genes and hypomethylation with activation of oncogenes. Supervised principal component analysis (SPCA) is a popular machine learning method. However, in a recent application to phenotype prediction from DNAm data SPCA was inferior to the specific method EVORA. Results We present Model-Selection-SPCA (MS-SPCA), an enhanced version of SPCA. MS-SPCA applies several models that perform well in the training data to the test data and selects the very best models for final prediction based on parameters of the test data. We have applied MS-SPCA for phenotype prediction from genome-wide DNAm data. CpGs used for prediction are selected based on the quantification of three features of their methylation (average methylation difference, methylation variation difference and methylation-age-correlation). We analysed four independent case–control datasets that correspond to different stages of cervical cancer: (i) cases currently cytologically normal, but will later develop neoplastic transformations, (ii, iii) cases showing neoplastic transformations and (iv) cases with confirmed cancer. The first dataset was split into several smaller case–control datasets (samples either Human Papilloma Virus (HPV) positive or negative). We demonstrate that cytology normal HPV+ and HPV- samples contain DNAm patterns which are associated with later neoplastic transformations. We present evidence that DNAm patterns exist in cytology normal HPV- samples that (i) predispose to neoplastic transformations after HPV infection and (ii) predispose to HPV infection itself. MS-SPCA performs significantly better than EVORA. Conclusions MS-SPCA can be applied to many classification problems. Additional improvements could include usage of more than one principal component (PC), with automatic selection of the optimal number of PCs. We expect that MS-SPCA will be useful for analysing recent larger DNAm data to predict future neoplastic transformations.
    Full-text · Article · Jun 2014 · BMC Bioinformatics
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    • "Previous studies done by our research team on ovarian cancer had demonstrated that the loss of GATA6 is responsible for the nuclear abnormalities and chromosomal numerical instability leading to cancer [10]. Thus the loss of GATA6led to all the hallmark of carcinoma and is prior to the initiation of tumor [8] [9] [10] making the loss of GATA6 a candidate biomarker to assess the risk for the initiation of the hallmark of cancer including cervical cancer. In 2011 statistical data of Ministry of Public Healthin Benin reported 1408 malignant tumors with 333 gynecological (24%). "
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    ABSTRACT: Abstract Introduction: GATA6 is a transcription factor which has role in the induction of cell differentiation genes and the maintenance of the differentiated state of epithelial cells. GATA6 expression is lost in neoplastic ovarian epithelia cells and in ovarian carcinoma leading to abnormal nuclear morphology characteristic of most cancer cells. We investigated the profile of GATA6 in cells collected from cervical-uterine smears (CUS) from women in the gynecologic service of three hospitals in Benin. Objective: To utilize GATA6 as molecular marker for the screening of women at risk of developing cervical carcinomas. Methods: CUS were collected from forty (40) women coming for regular checkup (a) at the National University Hospital (CNHU) in Cotonou and (b) from the local hospital of Mènontin (HZ) in Cotonou (south of Benin); (c) forty others (40) CUS were collected from women coming for treatment against HIV1 in the service of gynecology at the Departmental University Hospital (CHDU) of Borgou in Parakou (north of Benin). thus, GATA6 was analyzed in cells isolated from 80 CUS by immunoblotting techniques. Results: In women from Cotonou, GATA6 was present in 17/40 (42%) CUS, lightly expressed in 10/40 (25%) CUS and totally absent in 13/40 (32.5%) CUS. In the HIV1 infected women under treatment in Parakou, GATA6 was present in 8/40 (20%) CUS, lightly expressed in 13/40 (32.5%) and totally lost in 19/40 (47.5%) CUS. Conclusion: Our study showed that the loss of GATA6 in CUS was significantly higher in the population of women infected with HIV1 than in women from regular population in Cotonou. Thus the deficiency in GATA6 expression maybe utilized as diagnostic tools to identify women at risk for developing cervical carcinomas regardless of the infectious status before the onset of neoplasia. Keywords: GATA6, Cervical-Uterine smears, cervical carcinomas
    Full-text · Article · Jan 2014
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    • "As for the gene GATA4, it is expressed in most organs and plays a critical role in the development of these organs [19]. GATA4 is initially expressed during the formation of extraembryonic endoderm differentiated from the pluripotent embryonic stem cells of the inner cell mass during early embryonic development [20] and is also expressed in human ovarian epithelial cells [21,22]. However, GATA4 is often lost in ovarian cancer cells [21,23]. "
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    ABSTRACT: Tumor biomarkers are potentially useful in several ways such as the identification of individuals at increased risk of developing cancer, in screening for early malignancies and in aiding cancer diagnoses; tumor biomarkers may also be used for determining prognosis, predicting therapeutic response, patient tracking following curative surgery for cancer and for monitoring therapy. Epigenetic alterations, especially aberrant DNA methylation, are recognized as common molecular alterations in a variety of tumors and also occur during the development of tumors. The Cancer Grade Predictor (CGPredictor) is an extendable package with functions designed to facilitate systematic integrated and rapid analysis of high-throughput methylation through the use of most self-similarity subgroups of patients supported by various validating examinations with regarded to survival outcome to obtain the identity of the target predictor. We used high-grade serous ovarian cancer (HGSOC) and invasive breast carcinoma (BRCA) to demonstrate the usefulness of the CGPredictor package. The clustering results and the identity predictors worked well and efficiently in producing significant results after various tests were used to validate the usefulness of CGPredictor package. Also, some of the markers for either the HGSOC or BRCA marker panel have been previously reported to reveal significant results. Even when performed using a different platform with an independent large population BRCA dataset for validation, the identity predictor provided an accurate assessment of patient conditions and produced significant results. CGPredictor package is not a customized analysis tool designed specifically for the identification of only one or a few specific types of cancer but can be applied more broadly; moreover, the results indicate that the extracted predictors may worthy of consideration for further clinical testing to identify their potential usefulness for clinical molecular diagnosis and targeted treatments of patients with HGSOC and BRCA. So, the use of CGPredictor is feasible for examining the statistical significance of specific markers of interest and shows great potential for use with other types of cancers for cancer biomarker mining.
    Full-text · Article · Dec 2013 · BMC Systems Biology
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