Endometriosis: A Malignant Fingerprint.
Endometriosis: A Malignant Fingerprint.
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DOI：
10.17303/jcrto.2020.8.206
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发表时间：
2020-04-01
期刊：
影响因子：
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通讯作者：
Farias-Eisner, Robin
中科院分区：
文献类型：
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作者：
DeAngelo, Christopher;Tarasiewicz, Megan Burnett;Farias-Eisner, Robin
背景技术背景：子宫内膜异位症是复杂的，但识别新的生物标志物，炎症分子和遗传联系是增强子宫内膜异位症和子宫内膜异位症相关恶性肿瘤的检测，预测和治疗的关键。在这里，我们回顾了文献中有关的特定分子机制（S）介导的肿瘤发生内emiteratory.METHODS：指导（如科克伦）和发表的研究进行了鉴定。使用系统综述方法过滤器和作者的主题知识通过PubMed识别已发表的研究。这些数据进行了审查，以确定关键和相关的文章，创造一个全面的审查文章，以探讨与乳腺癌驱动的tumorigenes.Results相关的分子指纹：一个重要的焦点是C3 aR 1，PGR，ER 1，SOX-17和其他相关基因表达谱和乳腺癌驱动的肿瘤发生之间的联系。进一步的研究还应该集中在CA-125与HE-4的联合使用，以及OVA 1/MIA作为临床相关的诊断生物标志物在预测子宫内膜异位症驱动的肿瘤generation.CONCLUSIONS的作用：阐明子宫内膜异位症的分子指纹是为了了解驱动子宫内膜异位症相关恶性表型的分子机制。更好地了解这些基因的预测作用和生物标志物蛋白的价值将允许推导出独特的分子治疗算法，以更好地为我们的患者服务。
BACKGROUND: Endometriosis is complex, but identifying the novel biomarkers, inflammatory molecules, and genetic links holds the key to the enhanced detection, prediction and treatment of both endometriosis and endometriosis related malignant neoplasia. Here we review the literature relating to the specific molecular mechanism(s) mediating tumorigenesis arising within endometriosis.METHODS: Guidance (e.g. Cochrane) and published studies were identified. The Published studies were identified through PubMed using the systematic review methods filter, and the authors' topic knowledge. These data were reviewed to identify key and relevant articles to create a comprehensive review article to explore the molecular fingerprint associated with in endometriosis-driven tumorigenesis.RESULTS: An important focus is the link between C3aR1, PGR, ER1, SOX-17 and other relevant gene expression profiles and endometriosis-driven tumorigenesis. Further studies should also focus on the combined use of CA-125 with HE-4, and the role for OVA1/MIA as clinically relevant diagnostic biomarkers in the prediction of endometriosis-driven tumorigenesis.CONCLUSIONS: Elucidating endometriosis' molecular fingerprint is to understand the molecular mechanisms that drive the endometriosis-associated malignant phenotype. A better understanding of the predictive roles of these genes and the value of the biomarker proteins will allow for the derivation of unique molecular treatment algorithms to better serve our patients.