Thidiazuron suppresses breast cancer via targeting miR-132 and dysregulation of the PI3K–Akt signaling pathway mediated by the miR-202-5p–PTEN axis
Bibliographic record
Abstract
Chemo-resistance and metastasis are the most common causes of breast cancer recurrence and death. Thidiazuron (TDZ) is a plant growth regulator (phytohormone) whose biological effects on humans and animals has not yet been determined. In this study, we investigated the anticancer activity of this phytohormone on the drug resistant-triple negative breast cancer cell line MDA-MB-231. Treatment of the breast cancer cells with TDZ (1-50 μmol/L) caused more stressful environment and induced a significant increase in active caspase-positive cells. In addition, TDZ treatment (5 and 10 μmol/L) significantly attenuated the migration and the invasiveness of these highly metastatic cancer cells. Mechanistically, TDZ reduces cancer progression and invasiveness by targeting miR-202-5p, which stimulates the expression of phosphatase and tensin homolog (PTEN), the tumor suppressor that downregulates the PI3K-Akt signaling pathway. Treatment with TDZ significantly upregulates miRNA-132, the suppressor of breast cancer proliferation, which is also implicated in dysregulation of the TEN-Akt-NFκB signaling pathway. Interestingly, our molecular docking analysis revealed a potential non-covalent interaction between TDZ and Akt, PTEN, and PI3K. These findings suggest that TDZ suppresses breast cancer metastasis by targeting miRNA-132, the miR-202-5p-PTEN axis, and the PI3K-Akt signaling pathway downstream.
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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) | 0.001 | 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".