Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0476089 (endometrial cancer)
 11,379 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Progesterone and estrogen are critical regulators of uterine receptivity. To facilitate uterine remodeling for embryo attachment, estrogen activity in the uterine epithelia is attenuated by progesterone; however, the molecular mechanism by which this occurs is poorly defined. COUP-TFII (chicken ovalbumin upstream promoter transcription factor II; also known as NR2F2), a member of the nuclear receptor superfamily, is highly expressed in the uterine stroma and its expression is regulated by the progesterone-Indian hedgehog-Patched signaling axis that emanates from the epithelium. To further assess COUP-TFII uterine function, a conditional COUP-TFII knockout mouse was generated. This mutant mouse is infertile due to implantation failure, in which both embryo attachment and uterine decidualization are impaired. Using this animal model, we have identified a novel genetic pathway in which BMP2 lies downstream of COUP-TFII. Epithelial progesterone-induced Indian hedgehog regulates stromal COUP-TFII, which in turn controls BMP2 to allow decidualization to manifest in vivo. Interestingly, enhanced epithelial estrogen activity, which impedes maturation of the receptive uterus, was clearly observed in the absence of stromal-derived COUP-TFII. This finding is consistent with the notion that progesterone exerts its control of implantation through uterine epithelial-stromal cross-talk and reveals that stromal-derived COUP-TFII is an essential mediator of this complex cross-communication pathway. This finding also provides a new signaling paradigm for steroid hormone regulation in female reproductive biology, with attendant implications for furthering our understanding of the molecular mechanisms that underlie dysregulation of hormonal signaling in such human reproductive disorders as endometriosis and endometrial cancer.
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PMID:COUP-TFII mediates progesterone regulation of uterine implantation by controlling ER activity. 1759 85

Endometrial cancer (EC) is one of the most common malignancies in the female genital system, characterized by high mortality and recurrence rates. This study attempted to screen key genes and potential prognostic biomarkers for EC using bioinformatics analysis. Twenty-seven normal endometrial tissues and 135 EC samples were collected from four Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein-protein interaction (PPI) network. Finally, we assessed the prognostic values and molecular mechanism of the potential prognostic genes using the Kaplan-Meier curve and Gene Set Enrichment Analysis (GSEA). As a result, 28 upregulated and 94 downregulated genes were determined after gene integration of these four GEO data sets. Gene Ontology analysis indicated that DEGs were mainly involved in transcriptional regulation and cell proliferation. The Kyoto Encyclopedia of Gene and Genome pathway analysis primarily related to transcriptional misregulation and apoptosis. Moreover, the PPI analysis revealed 10 hub genes (JUN, UBE2I, GATA2, WT1, PIAS1, FOXL2, RUNXI, EZR, TCF4, and NR2F2) with a high degree of connectivity, among them, the expression tendency of nine genes except UBE2I were consistent with messenger RNA level from The Cancer Genome Atlas data. Furthermore, only FOXL2, TCF4, and NR2F2 were significantly correlated with prognosis of EC patients, and their low expression associated biological pathways were enriched in the cell cycle and fatty acid metabolism. In conclusion, this study identified three key genes as biomarkers and potential therapeutic targets of EC on the basis of integrated bioinformatics analysis. The findings will improve our comprehension of the molecular mechanisms underlying the pathogenesis and prognosis of EC.
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PMID:Identification of biomarkers correlated with diagnosis and prognosis of endometrial cancer using bioinformatics analysis. 3269 84