Identifying and optimizing human endometrial gene expression signatures for endometrial dating

Area of Science:

• Reproductive Medicine
• Genomics
• Molecular Biology

Background:

• Existing transcriptomic endometrial dating (TED) tools show variability in gene expression technology, gene content, and prediction models.
• A comparative analysis of different TED signatures is needed to understand their behavior and discrepancies.

Purpose of the Study:

• To develop an enhanced transcriptomic method for accurate secretory endometrial tissue dating.
• To compare the predictive performance of a novel gene signature against existing methods.

Main Methods:

• A multicenter prospective study involving 225 patients undergoing IVF treatment.
• Analysis of endometrial biopsies using a targeted RNA expression panel (ED panel) with 301 genes.
• Development of a new 73-gene signature (TED) and evaluation using artificial intelligence algorithms and external GEO datasets.

Main Results:

• The novel 73-gene TED signature demonstrated enhanced and consistent predictive capacity for secretory phase dating, particularly in the mid-secretory phase (P + 3-P + 8).
• The TED model outperformed previously published gene sets in accuracy and consistency across different datasets and technologies.
• The TED signature showed excellent performance in staging endometrial tissue within the window of implantation (average 114.5 ± 7.2 h post-progesterone).

Conclusions:

• Multiple gene signatures can date the endometrium, but performance varies based on gene selection, platform, algorithms, and dataset characteristics.
• The developed TED signature represents a next-generation tool for precise endometrial progression dating, especially in the mid-secretory phase.
• Further studies are required to confirm the clinical utility of endometrial dating methods and guide the specific application of TED.