DNA methylation age prediction model based on ovarian granulosa cells
10.3760/cma.j.cn101441-20250303-00103
- VernacularTitle:基于卵巢颗粒细胞的DNA甲基化预测卵巢生物学年龄的模型
- Author:
Peng LIU
1
;
Bowen ZHU
;
Yuheng LI
;
Liang WANG
;
Nan LIU
;
Ningxia SUN
Author Information
1. 海军军医大学第二附属医院生殖医学中心，上海 200003
- Publication Type:Journal Article
- Keywords:
DNA methylation;
Ovarian aging;
Ovarian biological age prediction model;
Next-generation DNA sequencing
- From:
Chinese Journal of Reproduction and Contraception
2025;45(5):442-447
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a novel ovarian biological age prediction model based on DNA methylation for ovarian aging assessment.Methods:A prospective cohort study method was used. From March 2024 to January 2025, we collected 96 ovarian granulosa cell samples of infertility patients due to fallopian tube factors or male factors from the Reproductive Medicine Center of the Second Affiliated Hospital of Naval Medical University. Then we analyzed DNA methylation patterns across five age-associated gene regions ( ELOVL2, miR29B2C, TRIM59, KLF14 and FHL2) in a discovery cohort comprising 63 human ovarian granulosa cell samples. Targeted bisulfite sequencing was performed through PCR amplification followed by next-generation DNA sequencing. Leveraging elastic net regression analysis, we developed a predictive model incorporating 29 methylation sites that demonstrated strong age correlation. The model was subsequently validated using an independent cohort comprising 33 human ovarian granulosa cell samples. Results:The DNA methylation age prediction model based on ovarian granulosa cells showed the following results in the discovery cohort as follows: median absolute error (MAE) was 2.534 ( R=0.742, P<0.001). In the independent validation cohort, MAE was 3.019 ( R=0.729, P<0.001). Conclusion:In this study, we utilized human ovarian granulosa cells as experimental samples to develop a novel DNA methylation-based model for predicting ovarian biological age. By integrating multiple methylation sites across five age-related gene regions, this model serves as a robust indicator of ovarian aging status.
