MicroRNA-34b Inhibits Pancreatic Cancer Metastasis Through Repressing Smad3
Bibliographic record
Abstract
Pancreatic cancer is characterized by extremely poor prognosis because of early recurrence and metastasis, and increasing evidence supports the critical role of microRNA in cancer progression. Here we identified that microRNA-34b functioned as a tumor-suppressing microRNA by targeting oncogenic Smad3 in pancreatic cancer. As a hypovascular tumor with a potential endoplasmic reticulum stress microenvironment, miR-34b was silenced after ER stress inducer thapsigargin (Tg) treatment and negatively regulated by ER stress chaperone glucose regulated protein 78 (GRP78) in pancreatic cancer cells. In human specimens, we found that miR-34b was down-regulated in pancreatic cancer tissues and low level of miR-34b expression was positively correlated with tumor-node-metastasis (TNM) stage, lymph-node metastasis and overall survival. Functional assays showed that over-expression of miR-34b inhibited pancreatic cancer progression in vitro and in vivo. In addition, Smad3 was demonstrated as a direct target of miR-34b and negatively regulated by miR- 34b at mRNA and protein levels. Luciferase assays confirmed that miR-34b could directly bind to the 3'untranslated region of Smad3. An inverse correlation between miR-34b and Smad3 was observed in 64 pancreatic cancer tissues. Our findings indicate that miR-34b acts as a tumor metastasis suppressor through negatively modulating Smad3, which may provide a potential therapeutic strategy for pancreatic 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) | 0.001 | 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".