Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles.

OBJECTIVE: To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure. DESIGN: Multicentric, prospective study. SETTING: Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory. PATIENTS: Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis. INTERVENTION(S): Endometrial biopsies collected in the mid-secretory phase. MAIN OUTCOME MEASURE(S): Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure. RESULTS: Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation. CONCLUSION(S): The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

Deciphering a shared transcriptomic regulation and the relative contribution of each regulator type through endometrial gene expression signatures.

BACKGORUND: While various endometrial biomarkers have been characterized at the transcriptomic and functional level, there is generally a poor overlap among studies, making it unclear to what extent their upstream regulators (e.g., ovarian hormones, transcription factors (TFs) and microRNAs (miRNAs)) realistically contribute to menstrual cycle progression and function. Unmasking the intricacies of the molecular interactions in the endometrium from a novel systemic point of view will help gain a more accurate perspective of endometrial regulation and a better explanation the molecular etiology of endometrial-factor infertility. METHODS: An in-silico analysis was carried out to identify which regulators consistently target the gene biomarkers proposed in studies related to endometrial progression and implantation failure (19 gene lists/signatures were included). The roles of these regulators, and of genes related to progesterone and estrogens, were then analysed in transcriptomic datasets compiled from samples collected throughout the menstrual cycle (n = 129), and the expression of selected TFs were prospectively validated in an independent cohort of healthy participants (n = 19). RESULTS: A total of 3,608 distinct genes from the 19 gene lists were associated with endometrial progression and implantation failure. The lists' regulation was significantly favoured by TFs (89% (17/19) of gene lists) and progesterone (47% (8 /19) of gene lists), rather than miRNAs (5% (1/19) of gene lists) or estrogen (0% (0/19) of gene lists), respectively (FDR < 0.05). Exceptionally, two gene lists that were previously associated with implantation failure and unexplained infertility were less hormone-dependent, but primarily regulated by estrogen. Although endometrial progression genes were mainly targeted by hormones rather than non-hormonal contributors (odds ratio = 91.94, FDR < 0.05), we identified 311 TFs and 595 miRNAs not previously associated with ovarian hormones. We highlight CTCF, GATA6, hsa-miR-15a-5p, hsa-miR-218-5p, hsa-miR-107, hsa-miR-103a-3p, and hsa-miR-128-3p, as overlapping novel master regulators of endometrial function. The gene expression changes of selected regulators throughout the menstrual cycle (FDR < 0.05), dually validated in-silico and through endometrial biopsies, corroborated their potential regulatory roles in the endometrium. CONCLUSIONS: This study revealed novel hormonal and non-hormonal regulators and their relative contributions to endometrial progression and pathology, providing new leads for the potential causes of endometrial-factor infertility.

Guidelines for biomarker discovery in endometrium: correcting for menstrual cycle bias reveals new genes associated with uterine disorders.

Transcriptomic approaches are increasingly used in reproductive medicine to identify candidate endometrial biomarkers. However, it is known that endometrial progression in the molecular biology of the menstrual cycle is a main factor that could affect the discovery of disorder-related genes. Therefore, the aim of this study was to systematically review current practices for considering the menstrual cycle effect and to demonstrate its bias in the identification of potential biomarkers. From the 35 studies meeting the criteria, 31.43% did not register the menstrual cycle phase. We analysed the menstrual cycle effect in 11 papers (including 12 studies) from Gene Expression Omnibus: three evaluating endometriosis, two evaluating recurrent implantation failure, one evaluating recurrent pregnancy loss, one evaluating uterine fibroids and five control studies, which collected endometrial samples throughout menstrual cycle. An average of 44.2% more genes were identified after removing menstrual cycle bias using linear models. This effect was observed even if studies were balanced in the proportion of samples collected at different endometrial stages or only in the mid-secretory phase. Our bias correction method increased the statistical power by retrieving more candidate genes than per-phase independent analyses. Thanks to this practice, we discovered 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis and 27 genes for recurrent implantation failure. In conclusion, we demonstrate that menstrual cycle progression masks molecular biomarkers, provides new guidelines to unmask them and proposes a new classification that distinguishes between biomarkers of disorder or/and menstrual cycle progression.

SARS-CoV-2 infection risk assessment in the endometrium: viral infection-related gene expression across the menstrual cycle.

OBJECTIVE: To determine the susceptibility of the endometrium to infection by-and thereby potential damage from-SARS-CoV-2. DESIGN: Analysis of SARS-Cov-2 infection-related gene expression from endometrial transcriptomic data sets. SETTING: Infertility research department affiliated with a public hospital. PATIENT(S): Gene expression data from five studies in 112 patients with normal endometrium collected throughout the menstrual cycle. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Gene expression and correlation between viral infectivity genes and age throughout the menstrual cycle. RESULT(S): Gene expression was high for TMPRSS4, CTSL, CTSB, FURIN, MX1, and BSG; medium for TMPRSS2; and low for ACE2. ACE2, TMPRSS4, CTSB, CTSL, and MX1 expression increased toward the window of implantation. TMPRSS4 expression was positively correlated with ACE2, CTSB, CTSL, MX1, and FURIN during several cycle phases; TMPRSS2 was not statistically significantly altered across the cycle. ACE2, TMPRSS4, CTSB, CTSL, BSG, and MX1 expression increased with age, especially in early phases of the cycle. CONCLUSION(S): Endometrial tissue is likely safe from SARS-CoV-2 cell entry based on ACE2 and TMPRSS2 expression, but susceptibility increases with age. Further, TMPRSS4, along with BSG-mediated viral entry into cells, could imply a susceptible environment for SARS-CoV-2 entry via different mechanisms. Additional studies are warranted to determine the true risk of endometrial infection by SARS-CoV-2 and implications for fertility treatments.

Uterine disorders affecting female fertility: what are the molecular functions altered in endometrium?

OBJECTIVE: To determine the molecular functions of genes exhibiting altered expression in the endometrium of women with uterine disorders affecting fertility. DESIGN: Retrospective analysis integrating case and control data from multiple cohorts with endometrium gene expression in women with uterine disorders. SETTING: Infertility research department affiliated with a university hospital. PATIENT(S): Two hundred and forty women, 121 of whom were controls, 119 of whom had endometrial adenocarcinoma (ADC), recurrent implantation failure (RIF), recurrent pregnancy loss (RPL), or stage II-IV endometriosis. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Genomewide gene expression and altered molecular functions in the endometrium of each uterine disorder. RESULT(S): Using robust analysis methods, we identified statistically significantly altered endometrial functions in all the uterine disorders. Cell cycle alterations were shared among all the pathologies investigated. Endometriosis was characterized by the down-regulation of ciliary processes. Among the endometriosis, ADC, and RIF samples, mitochondrial dysfunction and protein degradation were shared dysregulated processes. In addition, RPL had the most distinct functional profile, and 95% of affected functions were down-regulated. CONCLUSION(S): The most robust functions dysregulated in the endometrium of patients with uterine disorders across sample cohorts implicated an endometrial factor at the gene expression level. This shared endometrial factor affects endometrial receptivity processes.