Gene Expression Signature of Endometrial Samples from Women with and without Endometriosis

Abstract

Study objective: To develop a prototype of a complex gene expression biomarker for the diagnosis of endometriosis on the basis of differences between the molecular signatures of the endometrium from women with and without endometriosis.

Design: Prospective observational cohort study. Evidence obtained from a well-designed, controlled trial without randomization.

Setting: Department of reproductive medicine and surgery, A.I. Evdokimov Moscow State University of Medicine and Dentistry.

Patients: A total of 33 women (aged 32-38 years) were included in this study. Patients with and without endometriosis were divided into 2 separate groups. The group composed of patients with endometriosis 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-sequencing-profiled tissue samples (endocervix, ovarian surface epithelium) of 8 randomly selected healthy female cadaveric donors aged 32 to 38 years. The exclusion criteria for all patients were hormone therapy and any intrauterine device use for more than 1 year preceding surgery, as well as absence of other diseases of the uterus, fallopian tubes, and ovaries.

Interventions: Laparoscopic excision of endometriotic foci and hysteroscopy with endometrial sampling were performed. The cadaveric tissue samples included endocervix and ovarian surface epithelium. Endometrial sampling was obtained from the women in the control group. RNA sequencing was performed using Illumina HiSeq 3000 equipment (Illumina, Inc., San Diego, CA) 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 5 genes downregulated in the endometrium and endometriotic tissue of the patients with endometriosis, selected after comparison with the endometrium of the women without endometriosis. This gene signature showed a capacity for nearly perfect separation of all 52 analyzed tissue samples of the patients with endometriosis (endometrial as well as endometriotic samples) from the 14 tissue samples of both living and cadaveric donors without endometriosis (area under the curve = 0.982, Matthews correlation coefficient = 0.832).

Conclusion: The gene signature of the endometrium identified in this study may potentially serve as a nonsurgical diagnostic method for endometriosis detection. Our data also suggest that the statistical method of 5-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.