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<p>This study aimed to discover key genes associated with the in-vitro-fertilization embryo transfer (IVF‑ET) outcomes and develop new prediction models, as well as potential drugs. We enrolled the following infertile women who received IVF-ET between Sep 8, 2018, and Oct 19, 2021. The endometrial biopsy was performed for RNA sequencing. The differentially expressed genes (DEGs) were identified, based on which two models were developed for prediction of clinical pregnancy and live birth. Besides, we screened key pregnancy-lower and pregnancy-higher genes and identified candidate drugs that modulate the expression of key genes. Overall, 147 patients received IVF-ET, including 92 patients in the pregnancy group and 55 in the no-pregnancy group. We initially identified 180 differentially expressed genes (DEGs) between pregnancy and no-pregnancy groups, including 131 pregnancy-lower and 49 pregnancy-higher genes. After covariate-adjusted sensitivity analyses (age, RIF status, right-side AFC, and cycle type), 168 of these DEGs (93.3%) remained significant with consistent effect direction, and this 168-gene panel was used for all downstream predictive modelling and drug-repositioning analyses. Using the expression level of 17 genes and one clinical feature (recurrent implantation failure history), we developed a logistic regression model that achieved an apparent 100% classification accuracy in this internal dataset. Another live-birth prediction model was constructed using the expression of 20 genes and three clinical features (ovulation disorder, antral follicle count on the right side, and primary infertility), which also reached an apparent 100% accuracy in this internal cohort. Lastly, we screened key risk genes for drug mining, and four drugs may be beneficial for IVF‑ET: cyclosporine, acetaminophen, tretinoin, and estradiol. Combining key genes and clinical features, we developed two models with satisfactory apparent accuracy for predicting pregnancy and live birth. Based on the key genes, we further proposed four candidate drugs (cyclosporine, acetaminophen, tretinoin, and estradiol) that may have potential to improve IVF-ET outcomes and warrant further validation.</p> Graphical abstract <p></p>

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Novel Models to Predict Pregnancy for Patients Receiving In-Vitro-Fertilization Embryo Transfer: The Significance of Clinical Indicators and Key Gene Expression in the Endometrium

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Cheng Shi,
  • Yan-Bin Wang,
  • Huan Shen,
  • Fu-Mei Gao,
  • Min Fu,
  • Xi Chen,
  • Hong-Jing Han

摘要

This study aimed to discover key genes associated with the in-vitro-fertilization embryo transfer (IVF‑ET) outcomes and develop new prediction models, as well as potential drugs. We enrolled the following infertile women who received IVF-ET between Sep 8, 2018, and Oct 19, 2021. The endometrial biopsy was performed for RNA sequencing. The differentially expressed genes (DEGs) were identified, based on which two models were developed for prediction of clinical pregnancy and live birth. Besides, we screened key pregnancy-lower and pregnancy-higher genes and identified candidate drugs that modulate the expression of key genes. Overall, 147 patients received IVF-ET, including 92 patients in the pregnancy group and 55 in the no-pregnancy group. We initially identified 180 differentially expressed genes (DEGs) between pregnancy and no-pregnancy groups, including 131 pregnancy-lower and 49 pregnancy-higher genes. After covariate-adjusted sensitivity analyses (age, RIF status, right-side AFC, and cycle type), 168 of these DEGs (93.3%) remained significant with consistent effect direction, and this 168-gene panel was used for all downstream predictive modelling and drug-repositioning analyses. Using the expression level of 17 genes and one clinical feature (recurrent implantation failure history), we developed a logistic regression model that achieved an apparent 100% classification accuracy in this internal dataset. Another live-birth prediction model was constructed using the expression of 20 genes and three clinical features (ovulation disorder, antral follicle count on the right side, and primary infertility), which also reached an apparent 100% accuracy in this internal cohort. Lastly, we screened key risk genes for drug mining, and four drugs may be beneficial for IVF‑ET: cyclosporine, acetaminophen, tretinoin, and estradiol. Combining key genes and clinical features, we developed two models with satisfactory apparent accuracy for predicting pregnancy and live birth. Based on the key genes, we further proposed four candidate drugs (cyclosporine, acetaminophen, tretinoin, and estradiol) that may have potential to improve IVF-ET outcomes and warrant further validation.

Graphical abstract