<i>TET2</i> expedites coronary heart disease by promoting microRNA-126 expression and inhibiting the E2F3–PI3K–AKT axis
Bibliographic record
Abstract
DNA demethylases of the ten-eleven translocation (TET) family serve as tumor suppressors in various human cancers, but their pathogenic effects in coronary heart disease (CHD) remain unclear. Here we report that TET2 is transcriptionally upregulated in CHD patients, where it shows potential as a diagnostic tool. Mechanistic investigations revealed that TET2 facilitates inflammatory responses and cardiomyocyte apoptosis in rats through demethylation of microRNA-126 (miR-126) promoter. This interaction leads to sequestration of miR-126 from its target E2F transcription factor 3 (E2F3), contributing to E2F3 suppression in CHD. Upregulation of miR-126 when TET2 was silenced restored levels of inflammatory factors and aggravated the degree of cardiac injury and cardiomyocyte apoptosis in rats. By contrast, simultaneous overexpression of E2F3 and miR-126 reduced the levels of inflammatory factors, cardiac injury, and cardiomyocyte apoptosis in rats. Also, TET2 was found to regulate the activity of the PI3K–AKT pathway through the miR-126–E2F3 axis. Our findings uncover a novel function for TET2 in facilitating the progression of CHD.
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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 | 0.000 | 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) | 0.002 | 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".