Department of Gynecology, IVIRMA Barcelona S.L., Ronda del General Mitre, 14, 08017, Barcelona, Spain.
Group of Biomedical Research in Gynecology, Vall d'Hebron Research Institute, Barcelona, Spain.
J Assist Reprod Genet. 2020 Dec;37(12):2981-2987. doi: 10.1007/s10815-020-01965-6. Epub 2020 Oct 9.
To combine different independent endometrial markers to classify the presence of endometriosis.
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.
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.
结合不同的独立子宫内膜标志物对子宫内膜异位症进行分类。
从 109 名子宫内膜异位症患者和 110 名对照女性中获取子宫内膜活检。从文献中选择了 9 种不受周期阶段影响的候选生物标志物，并进行了 NanoString 分析。我们比较了组间差异表达的基因，并生成广义线性模型，以找到疾病的分类器。
广义线性模型正确地检测到 68%的子宫内膜异位症患者（包括深部浸润型和卵巢型子宫内膜异位症）。然而，与对照组相比，我们无法区分不同类型的子宫内膜异位症。在测试的 9 个基因中，FOS、MMP7 和 MMP11 似乎对疾病分类很重要，而 FOS 是子宫内膜异位症中表达最上调的基因。
虽然广义线性模型可以识别子宫内膜异位症，但我们没有用所选的候选基因获得完美的分类。
