Journal of assisted reproduction and genetics · 2020
External validation of putative biomarkers in eutopic endometrium of women with endometriosis using NanoString technology.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Vallvé-Juanico Júlia, López-Gil Carlos, Ponomarenko Julia, Melnychuk Taisiia, Castellví Josep, Ballesteros Agustín, Colás Eva, Gil-Moreno Antonio, Santamaria Costa Xavier
Аннотация
PURPOSE: To combine different independent endometrial markers to classify the presence of endometriosis.
METHODS: Endometrial biopsies were obtained from 109 women with endometriosis as well as 110 control women. Nine candidate biomarkers independent of cycle phase were selected from the literature and NanoString was performed. We compared differentially expressed genes between groups and generated generalized linear models to find a classifier for the disease.
RESULTS: Generalized linear models correctly detected 68% of women with endometriosis (combining deep infiltrating and ovarian endometriosis). However, we were not able to distinguish between individual types of endometriosis compared to controls. From the 9 tested genes, FOS, MMP7, and MMP11 seem to be important for disease classification, and FOS was the most over-expressed gene in endometriosis.
CONCLUSION(S): Although generalized linear models may allow identification of endometriosis, we did not obtain perfect classification with the selected gene candidates.
METHODS: Endometrial biopsies were obtained from 109 women with endometriosis as well as 110 control women. Nine candidate biomarkers independent of cycle phase were selected from the literature and NanoString was performed. We compared differentially expressed genes between groups and generated generalized linear models to find a classifier for the disease.
RESULTS: Generalized linear models correctly detected 68% of women with endometriosis (combining deep infiltrating and ovarian endometriosis). However, we were not able to distinguish between individual types of endometriosis compared to controls. From the 9 tested genes, FOS, MMP7, and MMP11 seem to be important for disease classification, and FOS was the most over-expressed gene in endometriosis.
CONCLUSION(S): Although generalized linear models may allow identification of endometriosis, we did not obtain perfect classification with the selected gene candidates.