Related Experiment Video
Updated: Jun 23, 2026
Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
Published on: September 25, 2018
Genome-wide nucleosome footprints of plasma cfDNA predict preterm birth: A case-control study
Zhiwei Guo1,2,3, Ke Wang4, Xiang Huang5
1Department of Obstetrics and Gynaecology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Insights
A new classifier, PTerm, accurately predicts spontaneous preterm birth using cell-free DNA promoter profiling. This method is easily adaptable for non-invasive prenatal testing, improving early detection of high-risk pregnancies.
Area of Science:
- Genomics
- Biomarker Discovery
- Computational Biology
Background:
- Preterm birth (PTB) affects 11% of global births, causing significant maternal and infant morbidity and mortality.
- Early identification of at-risk pregnancies is crucial for timely intervention and improved outcomes.
- Cell-free DNA (cfDNA) in plasma is a promising biomarker for monitoring pregnancy health and detecting complications like PTB.
Purpose of the Study:
- To develop and validate a novel classifier for predicting spontaneous preterm birth using cfDNA promoter profiling.
- To address the need for accurate, large-scale, and validated methods for PTB prediction.
Main Methods:
- A large-scale, multi-center case-control study of 2,590 pregnancies was conducted.
- Whole-genome sequencing of cfDNA was performed, focusing on promoter regions.
- Machine learning models, including support vector machines, were used to develop the PTerm classifier.
Main Results:
- The PTerm classifier, based on support vector machine modeling, achieved a high area under the curve (AUC) of 0.878 in cross-validation.
- PTerm demonstrated strong predictive performance in three independent validation cohorts, with an overall AUC of 0.849.
- The classifier effectively utilizes cfDNA promoter profiling for spontaneous preterm birth prediction.
Conclusions:
- PTerm exhibits high accuracy in predicting preterm birth.
- The PTerm classifier is easily adaptable for current non-invasive prenatal testing procedures without additional cost.
- This approach facilitates widespread preclinical adoption for early PTB risk assessment.
Background:
Preterm birth (PTB) occurs in approximately 11% of all births worldwide, resulting in significant morbidity and mortality for both mothers and their offspring. Identifying pregnancies at risk of preterm birth during early pregnancy may help improve interventions and reduce its incidence. Plasma cell-free DNA (cfDNA), derived from placenta and other maternal tissues, serves as a dynamic indicator of biological processes and pathological changes in pregnancy. These properties establish cfDNA as a valuable biomarker for investigating pregnancy complications, including PTB.
Methods And Findings:
To date, there are few methods available for PTB prediction that have been developed with large sample sizes, high-throughput screening, and validated in independent cohorts. To address this gap, we established a large-scale, multi-center case-control study involving 2,590 pregnancies (2,072 full-term and 518 preterm) from three independent hospitals to develop a spontaneous preterm birth classifier. We performed whole-genome sequencing on cfDNA, focusing on promoter profiling (read depth of promoter regions spanning from -1 to +1 kb around transcriptional start sites). Using four machine learning models and two feature selection algorithms, we developed classifiers for predicting preterm birth. Among these, the classifier based on the support vector machine model, named PTerm (Promoter profiling classifier for preterm prediction), exhibited the highest area under the curve (AUC) value of 0.878 (0.852-0.904) following leave-one-out cross-validation. Additionally, PTerm exhibited strong performance in three independent validation cohorts, achieving an overall AUC of 0.849 (0.831-0.866).
Conclusions:
In summary, PTerm demonstrated high accuracy in predicting preterm birth. Additionally, it can be utilized with current non-invasive prenatal test data without changing its procedures or increasing detection cost, making it easily adaptable for preclinical tests.
