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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714803 | PMC |
| http://dx.doi.org/10.1007/s10815-020-01965-6 | DOI Listing |
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