Gene regulation profiles by progesterone and dexamethasone in human endometrial cancer Ishikawa H cells
ABSTRACT Progesterone and glucocorticoids such as dexamethasone mediate distinct biological functions, yet they bind to receptors that recognize the same consensus DNA response element. In breast cancer, progestins are associated with the incidence and progression of tumors, whereas glucocorticoids are growth-suppressive in mammary cancer cells; the differential effects of these two steroids are less well understood in the hormone-dependent disease cancer of the uterine endometrium. We set out to identify genes that are regulated by progesterone through progesterone receptors and dexamethasone through glucocorticoid receptors in a well-differentiated human endometrial cancer cell line.
PR- and GR-positive Ishikawa H endometrial cancer cells were treated with vehicle, dexamethasone (100 nM) or progesterone (100 nM) for 2 h, 6 h, 12 h and 24 h, and RNA was isolated. Affymetrix microarrays were performed using the human HG-U133A chip, querying the expression of 22,000 genes. Expression of genes of particular interest was confirmed by real-time RT-PCR.
Expression analysis demonstrated that dexamethasone and progesterone regulate overlapping but distinct sets of genes and presumably exert many similar but also unique biological effects. Using real-time RT-PCR, we confirmed three particular genes of interest: the transcript for cysteine 1 (legumain), a gene associated with metastasis, that is strongly downregulated by progesterone, upstream c-fos relating transcription factor-2 (USF-2), an anti-proliferative factor that is induced by both progesterone and dexamethasone and N-cadherin, a cellular adhesion molecule downregulated by dexamethasone.
These studies provide new insight into the effects of progesterone and dexamethasone in endometrial cancer cells and provide an extensive list of regulated pathways which can be assessed in the future as biomarkers and molecular targets for new therapies. Taken together, our findings indicate that progesterone and dexamethasone are primarily growth inhibitors in Ishikawa H endometrial cancer cells.
SourceAvailable from: Alison J Tyson-Capper[Show abstract] [Hide abstract]
ABSTRACT: Overexpression of human epidermal growth factor receptor (HER-2) occurs in 20-30% of breast cancers and confers survival and proliferative advantages on the tumour cells making HER-2 an ideal therapeutic target for drugs like Herceptin. Continued delineation of tumour biology has identified splice variants of HER-2, with contrasting roles in tumour cell biology. For example, the splice variant Δ16HER-2 (results from exon 16 skipping) increases transformation of cancer cells and is associated with treatment resistance; conversely, Herstatin (results from intron 8 retention) and p100 (results from intron 15 retention) inhibit tumour cell proliferation. This review focuses on the potential clinical implications of the expression and coexistence of HER-2 splice variants in cancer cells in relation to breast cancer progression and drug resistance. "Individualised" strategies currently guide breast cancer management; in accordance, HER-2 splice variants may prove valuable as future prognostic and predictive factors, as well as potential therapeutic targets.International Journal of Cell Biology 01/2013; 2013:973584. DOI:10.1155/2013/973584
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ABSTRACT: The fundamental rationale for the use of microarray-based gene expression profiling to characterize biological samples is based in part on the principle that cells, tissues, and perturbations applied to them can be characterized on the basis of their relative expression of genes and transcripts. Different biological states, cell types, and influences can be distinguished based on transcriptional profiles and the change in the relative levels of different genes and gene groups. This genomic expression profile-based discovery of biological states and effector-actions represents an essential element of a systems-based whole-genome approach to characterizing cells and tissues, and differs from the characterization of individual gene expression changes in isolation from one another, and has the potential to increase knowledge in all fields of biomedicine. The past two decades have seen a paradigm shift in which medical genetics has moved from being a tool of the basic investigator to play a role in the mainstream of medical practice. Identification of genetic causal agents of common endocrine disorders, deciphering underlying molecular pathophysiology of known conditions, development of new predictive tests for genetic abnormalities, and applications in the field of therapeutics are some of the implications of this shift. Endocrine systems, in particular, offer tremendous opportunities for the use of genomic analyses to understand physiological and pathological responses and effectors without being biased to a particular gene or set of genes. Therefore, the responses of diverse and potentially diversely affected systems can be broadly evaluated, constrained only by the limitation that there may be either a primary or secondary impact on transcript abundance. This emerging concept—endocrinomics—thus has the potential to significantly impact the field of endocrine research and clinical practice. However, advancements in the field are also limited by problems in collecting comprehensive datasets, the inherent complexity of multiple interacting systems, genetic variations between individuals, and some cumbersomeness associated with expression profiling technology and data analysis itself. This chapter discusses some of the issues to be considered in the design and analysis of microarray experiments for the characterization of endocrine-regulated systems.
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ABSTRACT: Endometrial cancer, the most common gynecologic malignancy in the United States, is on the rise, and survival is worse today than 40 years ago. In order to improve the outcomes, better biomarkers that direct the choice of therapy are urgently needed. In this review, we explore the estrogen receptor as the most studied biomarker and the best predictor for response for endometrial cancer reported to date.Obstetrics and Gynecology International 01/2013; 2013:479541. DOI:10.1155/2013/479541