Embryo quality predictive models based on cumulus cells gene expression.

Area of Science:

• Reproductive biology
• Biomarker discovery
• In vitro fertilization (IVF) technologies

Background:

• Cumulus cells (CC) surrounding oocytes possess unique gene expression profiles linked to embryo developmental potential.
• Identifying reliable biomarkers for high-quality embryo selection is crucial for optimizing infertility treatment success.

Purpose of the Study:

• To investigate the potential of combining multiple gene expression biomarkers from CC for improved prediction of embryo development.
• To evaluate the predictive power of specific gene expression patterns in CC for differentiating high-quality from low-quality embryos.

Main Methods:

• Gene expression analysis using quantitative real-time polymerase chain reaction (qPCR) on 58 CC samples from 17 IVF patients.
• Analysis of five candidate genes, focusing on AMHR2 and LIF, in relation to embryo quality.
• Development and comparison of two prediction models: binary logistic regression and decision tree analysis.

Main Results:

• Significant differences in gene expression levels were observed for AMHR2 and LIF between high-quality and low-quality embryos.
• The binary logistic model achieved an Area Under the Curve (AUC) of 0.72 ± 0.08.
• The decision tree model achieved a similar AUC of 0.73 ± 0.03, offering easily interpretable clinical rules.

Conclusions:

• AMHR2 and LIF gene expression in cumulus cells are promising biomarkers for predicting embryo quality in IVF.
• Both logistic regression and decision tree models demonstrate comparable predictive accuracy.
• The decision tree model's straightforward rules enhance its clinical applicability for embryo selection in infertility treatment.