Exploring Differentially Methylated Genes among Preterm Birth and Full-Term Birth
Bibliographic record
Abstract
INTRODUCTION: Preterm birth (PTB) is a major contributor to neonatal morbidity and mortality. DNA methylation plays a critical role in fetal development and may serve as an epigenetic biomarker for PTB. However, few epigenetic studies have investigated PTB-specific DNA methylation changes. This study aimed to identify epigenetic differences between PTB and term birth (TB) infants. METHODS: A total of 218 cord blood samples from three independent PTB studies were analyzed to identify epigenetic differences between PTB and TB infants. Differential methylation analysis was conducted while adjusting for key covariates, including gestational age, sex, and disease status. Differentially methylated regions (DMRs) (genes and promoters) and differentially methylated sites (DMSs) (CpG sites) were assessed for significant methylation differences between the two groups. RESULTS: In PTB infants, several genes, including RNASE3, HGF, CLEC5A, LIPN, NXF1, and CCDC12 showed significant hypermethylation (p < 0.05), while the MUC20 and IFNL4 genes showed significant hypomethylation (p < 0.05). The eForge analysis revealed that hypermethylated (p < 0.05) CpG sites were significantly enriched in different fetal tissues such as the small and large intestines, adrenal gland, fetal heart, lungs, and kidney, whereas hypomethylated CpGs showed no significant enrichment. Gene ontology analysis indicated that differentially methylated genes were primarily involved in immune response regulation. Notably, S100A9 and S100A8 genes, which play crucial roles in neonatal immune function and sepsis risk, were hypermethylated (p < 0.05) in PTB infants. CONCLUSION: This study identified PTB-associated DNA methylation changes in immune-related genes, suggesting their potential epigenetic biomarkers for PTB. These findings enhance our understanding of PTB pathogenesis and may contribute to the development of novel diagnostic and therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".