A miR-34a-guided, tRNAiMet-derived, piR_019752-like fragment (tRiMetF31) suppresses migration and angiogenesis of breast cancer cells via targeting PFKFB3
Bibliographic record
Abstract
Abstract Although we recently demonstrated that miR-34a directly targets tRNA i Met precursors via Argonaute 2 (AGO2)-mediated cleavage, consequently attenuating the proliferation of breast cancer cells, whether tRNA i Met fragments derived from this cleavage influence breast tumor angiogenesis remains unknown. Here, using small-RNA-Seq, we identified a tRNA i Met -derived, piR_019752-like 31-nt fragment tRiMetF31 in breast cancer cells expressing miR-34a. Bioinformatic analysis predicted 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) as a potential target of tRiMrtF31, which was validated by luciferase assay. tRiMetF31 was downregulated, whereas PFKFB3 was overexpressed in cancer cell lines. Overexpression of tRiMetF31 profoundly inhibited the migration and angiogenesis of two breast cancer cell lines while slightly inducing apoptosis. Conversely, knockdown of tRiMetF31 restored PFKFB3-driven angiogenesis. miR-34a was downregulated, whereas tRNA i Met and PFKFB3 were upregulated in breast cancer, and elevated PFKFB3 significantly correlated with metastasis. Our findings demonstrate that tRiMetF31 profoundly suppresses angiogenesis by silencing PFKFB3, presenting a novel target for therapeutic intervention in breast cancer.
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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".