Cervical Secretion Methylation Is Associated with the Pregnancy Outcome of Frozen-Thawed Embryo Transfer.
Yi-Xuan LeePo-Hsuan SuQuang Anh DoChii-Ruey TzengYu-Ming HuChi-Huang ChenChien-Wen ChenChi-Chun LiaoLin-Yu ChenYu-Chun WengHui-Chen WangHung-Cheng LaiPublished in: International journal of molecular sciences (2023)
The causes of implantation failure remain a black box in reproductive medicine. The exact mechanism behind the regulation of endometrial receptivity is still unknown. Epigenetic modifications influence gene expression patterns and may alter the receptivity of human endometrium. Cervical secretions contain endometrial genetic material, which can be used as an indicator of the endometrial condition. This study evaluates the association between the cervical secretion gene methylation profile and pregnancy outcome in a frozen-thawed embryonic transfer (FET) cycle. Cervical secretions were collected from women who entered the FET cycle with a blastocyst transfer (36 pregnant and 36 non-pregnant women). The DNA methylation profiles of six candidate genes selected from the literature review were measured by quantitative methylation-specific PCR (qMSP). Bioinformatic analysis of six selected candidate genes showed significant differences in DNA methylation between receptive and pre-receptive endometrium. All candidate genes showed different degrees of correlation with the pregnancy outcomes in the logistic regression model. A machine learning approach showed that the combination of candidate genes' DNA methylation profiles could differentiate pregnant from non-pregnant samples with an accuracy as high as 86.67% and an AUC of 0.81. This study demonstrated the association between cervical secretion methylation profiles and pregnancy outcomes in an FET cycle and provides a basis for potential clinical application as a non-invasive method for implantation prediction.
Keyphrases
- pregnancy outcomes
- dna methylation
- pregnant women
- genome wide
- gene expression
- copy number
- machine learning
- endothelial cells
- endometrial cancer
- type diabetes
- metabolic syndrome
- artificial intelligence
- density functional theory
- big data
- deep learning
- high resolution
- risk assessment
- preterm birth
- human health
- insulin resistance
- electron transfer