Study objective: to develop a prototype of complex gene expression biomarker for the diagnosis of endometriosis based on differences between molecular signatures of endometrium from women with and without endometriosis.
Design: Prospective observational cohort study. II-1. Evidence obtained from a well-designed controlled trial without randomization.
Setting: Department of Reproductive Medicine and Surgery of A.I. Evdokimov Moscow State University of Medicine and Dentistry.
Patients: A total of 33 women (age 32-38 years) were included in this study. Patients with and without endometriosis were divided into two separate groups. The group of endometriosis patients included 19 living patients with endometriosis who underwent laparoscopic excision of endometriosis. The control group included 6 living patients who underwent laparoscopic excision of incompetent uterine scar after cesarean section with both surgically and histologically confirmed absence of endometriosis and adenomyosis. An additional control/verification group included various previously RNA-seq profiled tissue samples (endocervix, ovarian surface epithelium) of 8 randomly selected healthy female cadaveric patients of 32-38 years of age. Exclusion criteria for all patients were hormone therapy and any intrauterine device use for over 1 year preceding surgery; absence of other diseases of the uterus, fallopian tubes, and ovaries.
Interventions: Laparoscopic excision of endometriotic foci and hysteroscopy with endometrial sampling were performed. Cadaveric tissue samples included endocervix and ovarian surface epithelium. Endometrial sampling was obtained from women of the control group. RNA sequencing was done using Illumina HiSeq 3000 equipment for single-end sequencing. Unique bioinformatics algorithms were developed and validated using experimental and public gene expression datasets.
Measurements and main results: We generated a characteristic signature of five genes downregulated in endometrium and endometriotic tissue of patients with endometriosis, selected after comparison to endometrium of women without endometriosis. This gene signature showed a capacity of nearly perfect separation of all 52 analyzed tissue samples of patients with endometriosis (both their endometrial and endometriotic samples) from 14 tissue samples of both living and cadaveric patients without endometriosis (AUC=0.982, Matthews correlation coefficient MCC=0.832).
Conclusion: The gene signature of endometrium identified in this study may potentially serve as a non-surgical diagnostic method for endometriosis detection. Our data also suggest that the statistical method of five-fold cross validation of differential gene expression analysis can be used to generate robust gene signatures using real-world clinical data.
Keywords: Big data in clinical medicine; Endometriosis; Gene expression signature; Molecular diagnostics; RNA sequencing.
Copyright © 2021. Published by Elsevier Inc.