Maternal blood <i>EBF1</i>-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study
Bibliographic record
Abstract
Both genetic variants and maternal blood mRNA levels of EBF1 gene have been linked to sPTB. Animal and human studies suggest that specific EBF1-based miRNAs are involved in various physiological and pathophysiological processes. However, to date, we did not find any reports of EBF1-based miRNAs or miRNA transcripts in relation to sPTB. We therefore aimed to examine whether maternal blood early B cell factor 1 (EBF1) gene-based microRNA (miRNA) transcripts can be used for detecting risk of spontaneous preterm birth (sPTB). We conducted a nested case-control study within a Canadian cohort consisting of 1878 singleton pregnancies enrolled from May 2008 to December 2010 in Calgary, Alberta, Canada. We used a public gene expression dataset (GSE59491) derived from maternal blood in trimesters 2–3 that included women with sPTB (n = 51) and term births (n = 106) matched for maternal age, race/ethnicity, pre-pregnancy body mass index, smoking during pregnancy, and parity within the Canadian cohort. Two bioinformatics tools, miRWalk and STarMirDB, with different algorithms were applied to retrieve miRNA transcripts that putatively target the EBF1 gene (i.e. EBF1-based). Limma moderated t-tests were used to examine differentially expressed (DE) miRNA transcripts (sPTB vs term) within trimesters. Logistic regression models with miRNA transcript tertiles were applied to assess threshold associations between candidate miRNA transcripts’ levels and sPTB. Receiver operating characteristic (ROC) analyses were used to identify the maximum Youden Index and its corresponding optimal sensitivity/specificity cut-point of EBF1-based miRNA transcripts for classifying sPTB, and to compare the classification performance of a linear combination (score) of miRNA transcripts with that of individual miRNA transcripts. A five-fold cross-validation was applied to examine the possible overfitting problem of the final ROC model. Four maternal blood EBF1-based miRNA transcripts (MIR4266, MIR1251, MIR601, MIR3612) in the 3rd trimester were significantly associated with sPTB. The odds ratios (95%CIs) for highest versus lowest tertile of the four miRNA transcripts were 3.01–5.25(1.21–13.14, p ≤ .018). The combined 4-miRNA transcripts’ score significantly improved the classification of sPTB compared to individual miRNA transcripts (AUC increased from 0.65–0.69 to 0.82, p ≤ .0034) and showed a sensitivity for sPTB of 0.81 and a specificity of 0.72. The final ROC model of the EBF1-based 4 miRNA transcripts’ score in cases and controls had no significant overfitting issue. Maternal blood EBF1-based miRNA transcripts may, along with other biomarkers, be useful in screening for sPTB risk in 3rd trimester. Our results also provide clues for further study of potential molecular mechanisms underlying the relationship between EBF1 gene and sPTB, e.g. connecting genetic variants, mRNA expression, and miRNA regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".