Transcriptomic analysis of endometrial receptivity for a genomic diagnostics model of Chinese women

For transcriptomic analysis of endometrial receptivity in a cohort of Chinese women, a predictor constructed for endometrial dating had 100% accuracy in training set and 85.19% accuracy in validation set.

VOLUME 116, ISSUE 1, P157-164


Wen-bi Zhang, Ph.D., Qing Li, B.S., Hu Liu, M.S., Wei-jian Chen, Ph.D., Chun-lei Zhang, M.S., He Li, Ph.D., Xiang Lu, Ph.D., Jun-ling Chen, Ph.D., Lu Li, M.S., Han Wu, Ph.D., Xiao-xi Sun, Ph.D. 



To define the transcriptomic signature with respect to human endometrial receptivity in Chinese women by next-generation sequencing and to develop a more refined and customized bioinformatic predictive method for endometrial dating in Chinese women.




A tertiary hospital−based reproductive medicine center.


Ninety healthy, fertile Chinese women.


Human endometrial biopsies.

Main Outcome Measure(s)

Gene expression of endometrial biopsies.


Ninety endometrial samples from healthy Chinese women during their menstrual cycles—including prereceptive (luteinizing hormone [LH] + 3 days/LH + 5 days), receptive (LH + 7 days), and post-receptive (LH + 9 days) phases—were subjected to transcriptomic analysis using messenger RNA (mRNA)-enriched RNA-Seq. Feature genes were obtained and used to train the predictor for endometrial dating, with 63 samples for the training set and 27 samples for the validation set. Differentially expressed genes (DEGs) were identified by comparing samples from different phases of the menstrual cycle. Based on the transcriptomic feature genes, we constructed a bioinformatic predictor for endometrial dating. The accuracy on assessment of the endometrium on days LH + 3, LH + 5, LH + 7, and LH + 9 was 100% in the training set and 85.19% in the validation set.


Our transcriptomic profiling method can be used to monitor the window of implantation with regard to the endometrium in the Chinese population. This method potentially provides an evaluation of endometrial status, and can be used to predict a personal window of implantation by reproductive medicine clinicians.

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