Preterm birth (PTB) occurs in around 11% of all births worldwide, resulting in significant morbidity and mortality for both mothers and offspring. Identification of pregnancies at risk of preterm birth in early pregnancy may help improve intervention and reduce its incidence. However, there exist few methods for PTB prediction developed with large sample size, high throughput screening and validation in independent cohorts. Here, we established a large scale, multi center, and case control study that included 2,590 pregnancies (2,072 full term and 518 preterm pregnancies) from three independent hospitals to develop a preterm birth classifier. We implemented whole genome sequencing on their plasma cfDNA and then their promoter profiling (read depth spanning from -1 KB to +1 KB around the transcriptional start site) was analyzed. Using three machine learning models and two feature selection algorithms, classifiers for predicting preterm delivery were developed. Among them, a classifier based on the support vector machine model and backward algorithm, named PTerm (Promoter profiling classifier for preterm prediction), exhibited the largest AUC value of 0.878 (0.852-0.904) following LOOCV cross validation. More importantly, PTerm exhibited good performance in three independent validation cohorts and achieved an overall AUC of 0.849 (0.831-0.866). Taken together, PTerm could be based on current noninvasive prenatal test (NIPT) data without changing its procedure or adding detection cost, which can be easily adapted for preclinical tests.

The authors have declared no competing interest.

This work was supported by project grants from the National Natural Science Foundation of China [81600404, 81871177, 82173001]; Medical and health key projects of Zhongshan [2020b3011]; Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation [201905010003]; Wu Jieping Medical Foundation [320.6750.19089-73]; Medical Scientific Research Foundation of Guangdong Province [A2022104].

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Ethics committees of Jiangmen maternal & child healthcare hospital gave ethical approval for this work.

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All data produced in the present study are available upon reasonable request to the authors