Identification of microRNA signature in the progression of gestational trophoblastic disease
Bibliographic record
Abstract
Gestational trophoblastic disease (GTD) encompasses a range of trophoblast-derived disorders. The most common type of GTD is hydatidiform mole (HM). Some of HMs can further develop into malignant gestational trophoblastic neoplasia (GTN). Aberrant expression of microRNA (miRNA) is widely reported to be involved in the initiation and progression of cancers. MiRNA expression profile also has been proved to be the useful signature for diagnosis, staging, prognosis, and response to chemotherapy. Till now, the profile of miRNA in the progression of GTD has not been determined. In this study, a total of 34 GTN and 60 complete HMs (CHM) trophoblastic tissues were collected. By miRNA array screening and qRT-PCR validating, six miRNAs, including miR-370-3p, -371a-5p, -518a-3p, -519d-3p, -520a-3p, and -934, were identified to be differentially expressed in GTN vs. CHM. Functional analyses further proved that miR-371a-5p and miR-518a-3p promoted proliferation, migration, and invasion of choriocarcinoma cells. Moreover, we demonstrated that miR-371a-5p was negatively related to protein levels of its predictive target genes BCCIP, SOX2, and BNIP3L, while miR-518a-3p was negatively related to MST1 and EFNA4. For the first time, we proved that miR-371a-5p and miR-518a-3p directly targeted to 3'-UTR regions of BCCIP and MST1, respectively. Additionally, we found that miR-371a-5p and miR-518a-3p regulated diverse pathways related to tumorigenesis and metastasis in choriocarcinoma cells. The results presented here may offer new clues to the progression of GTD and may provide diagnostic biomarkers for GTN.
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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".