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It is well-known that the clinical outcomes are different between type 1 (estrogen dependent) and type 2 (estrogen independent) endometrial cancer. Studies have suggested that the estrogen receptor (ER) is positively correlated with endometrial cancer survival, however we previously reported that there is no difference in the positivity of ER as well as sex hormone levels between subtypes of cancer. G-protein-coupled receptor-30 (GPR 30), an alternative estrogen receptor has been suggested to be negatively correlated with clinical outcomes of endometrial cancer. In this study we investigated whether the positivity of GPR30 is different between subtypes of cancer. The immunostaining of GPR30 and ER was examined and analysed in 128 cases taking into account menopausal status. Overall, 105 (82%) cases were GPR30 positive and 118 (92%) cases were ER positive. The positivity of GPR30 in type 1 endometrial cancer (83%) was not statistically different to type 2 endometrial cancer (78%). In addition, intensity of immunostaining of GPR30 in type 1 endometrial cancer was also not different to type 2 endometrial cancer quantified by semi-quantitative analysis (p = 0.268). Menopausal status was not associated with the positivity of GPR30 in both type 1 and type 2 endometrial cancer. Furthermore, the positivity and intensity of immunostaining of GPR30 were not correlated with the positivity and intensity of immunostaining of ER in endometrial cancer (p = 0.689). Our data further confirm that type 2 endometrial cancer may not be completely estrogen independent, and suggest that type 1 and type 2 endometrial cancer may have similar pathogenesis.
Pubmed ID: 29207611
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THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 1st, 2022. Software application for genetic analysis of classical biometric traits like blood pressure or height that are caused by a combination of polygenic inheritance and complex environmental forces. (entry from Genetic Analysis Software)
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsAn Antibody supplier; Dako was purchased by Agilent in 2012 and several years later the websites began to reflect the Dako products as part of the Agilent catalog.
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