Cheap Cell-Free DNA Based Test Accurately Predicts Preterm Birth
By LabMedica International staff writers Posted on 23 Apr 2025 |

Preterm birth (PTB) occurs in around 11% of all births globally, leading to considerable morbidity and mortality for both mothers and their newborns. Identifying pregnancies at risk of PTB early in gestation can enhance intervention strategies and help reduce the incidence of this condition. A new study has demonstrated that a low-cost, non-invasive test utilizing cell-free DNA (cfDNA) collected during routine early pregnancy testing may serve as a method to predict the risk of PTB.
In a large, multi-center case-control study conducted by South China University of Technology (Guangzhou, China), the research team assessed the potential of cfDNA for predicting PTB. The researchers gathered cfDNA via whole-genome sequencing, focusing on promoter profiling. Samples were collected between 12 to 28 weeks of gestation from three independent hospitals in China. Exclusion criteria included multiple pregnancies, chorioamnionitis, uterine fibroids, congenital abnormalities, and the use of assisted reproductive technologies. Participants were grouped based on their delivery outcome, including spontaneous PTB before 37 weeks of gestation and full-term delivery after 38 weeks. Matching was done based on gestational age at the time of sampling, maternal age, and body mass index.
The necessary gene information was sourced from the Ref-Seq of the University of California Santa Cruz Genome Browser Database, with promoter regions ranging from -1 to 1 kb for each transcript. Raw reads were aligned to the human reference genome using Bwa-mem (ver. 3.5.0). Expression profiles for placenta and whole blood were assessed through the Gene Expression Omnibus database. To analyze gene expression, the researchers identified the top 500 most highly expressed genes and the 500 least expressed genes. A P-value was calculated from the primary transcription start site (pTSS) coverages between preterm and full-term samples. Functional relationships from the String database were used to construct a gene correlation network. Classifiers for predicting spontaneous PTB (sPTB) were developed based on whole-genome sequencing. The analysis included whole-genome sequencing data from 20 preterm pregnancies and 20 full-term pregnancies, along with RNA expression profiles from preterm pregnancies.
In the comparison of pTSS coverage between the 500 most expressed genes and the 500 least expressed genes, it was found that the most expressed genes had reduced depth at their pTSS regions. Lower read depth was also observed in housekeeping genes with high expression levels, while higher read depth was seen in genes with little expression. The maternal whole blood data showed similar trends, suggesting a correlation between plasma cfDNA coverage at pTSS regions and the expression profiles of the original tissues. The study also evaluated the cfDNA profiles of platelet-enriched genes, finding reduced coverage at the pTSS regions of these genes in pregnancies that resulted in PTB compared to those with full-term deliveries. This suggests that promoter profiling might differ significantly between these two groups.
Further analysis of cfDNA promoter profiling revealed 277 genes with differential coverage at the pTSS. Of these, 146 had increased coverage, while 131 had decreased coverage. Notably, key genes associated with PTB incidence included ERBB2, ESR1, NFKBIA, HSPA5, PRKCB, RAF1, NFE2LE, SNAI1, GSN, and ATF3. These findings, published in PLOS Medicine, suggest that the promoter-profiling-based classifier (PTerm) has potential for predicting PTB risk early in pregnancy. The researchers concluded that this method could be easily integrated into non-invasive prenatal testing without the need for additional procedures or higher costs.
“Currently, PTerm can distinguishing PTB pregnancies from full-term pregnancies with high accuracy. Moving forward, leveraging additional data on promoter profiles across different gestational ages could facilitate developing a model for accurately predicting delivery time,” stated the researchers.
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