MiR-448 targets BLC2 and inhibits the growth of pituitary adenoma cells
Bibliographic record
Abstract
There is an increasing body of evidence indicating the important roles of miRNAs in the progression of pituitary adenoma. Recent studies have shown decreased expression and tumor suppressive function of miR-448 in cancers; however, the clinical significance of miR-448 in pituitary adenoma has remained largely unknown. In our study, we found that miR-448 was down-regulated in pituitary adenoma tissues and cell lines. Overexpression of miR-448 significantly inhibited the proliferation and migration of pituitary adenoma cells. Increased cell apoptosis was also observed with overexpression of miR-448. To further understand the mechanisms behind the regulation of pituitary adenoma by miR-448 in, the targets of miR-448 were predicted using the bioinformatics tools. B cell lymphoma 2 (BCL2) was identified as a target of miR-448. MiR-448 bound the 3'-untranslated region (UTR) of BCL2 and inhibited the expression of BCL2 in pituitary adenoma cells. There was a consistent and significantly negative correlation between the level of miR-448 and BCL2 in pituitary adenoma tissues. When BCL2 was highly expressed, the inhibitory impact of miR-448 on the proliferation and apoptosis of pituitary adenoma cells was significantly inhibited. Collectively, our findings emphasize the significance of the miR-448-BCL2 axis in the development of pituitary adenoma, highlighting the potential therapeutic significance of miR-448 in pituitary adenoma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".