Noninvasive pre-transfer identification of women with recurrent implantation failure using a plasma microRNA–based machine learning model

Recurrent implantation failure (RIF) refers to patients who have undergone at least three implantation failures with several high-quality embryos. Identifying patients with RIF in advance is crucial for effective clinical managements. However, uterine biopsy, the primary research approach, is not only invasive but also poses a risk to the integrity of the endometrial microenvironment. Here, we provided a novel, safe, and immediate methodology for RIF populations identification through plasma microRNA (miRNA) profiles. Peripheral blood samples of 163 women were collected and sequenced to quantify the relative expression levels of plasma miRNAs. Through a comparative analysis of the temporal expression between patients with RIF and those with successful embryo implantation during the peri-implantation period, we determined that the RIF-associated miRNA signature was established at the early stage of the window of implantation (WOI). By global screening, we identified 217 miRNAs that were differentially expressed between groups on the ovulation day (D0), using a threshold of |fold change| ≥ 1.5. Through refining selection criteria, 10 eligible miRNAs were ultimately selected and further validated through real-time quantitative polymerase chain reaction (RT-qPCR). These miRNAs were employed to construct a logistic regression-based RIF model. The model demonstrated robust performance in the validation and test cohorts, with area under the curve (AUC) of 0.947 and 0.912, respectively. We also demonstrated that miRNAs maintain relative stability within a specific phase, making them suitable as clinical biomarkers. This study provides a noninvasive methodology for identifying women with RIF and suggests a potential role in predicting RIF risk before embryo transfer.