miR-1247 is Correlated with Prognosis of Pancreatic Cancer and Inhibits Cell Proliferation by Targeting Neuropilins
Bibliographic record
Abstract
Accumulating evidence indicates that microRNAs (miRNAs) have great potential as tumor biomarkers and therapeutic agents owing to their functions in tumorigenesis and cancer progression. Aberrant expression of miR-1247 has been found in several cancers and is predicted to play an important role in the pathological processes of pancreatic cancer by miRNA-regulated network analysis. We investigated the expression profile of miR-1247 in pancreatic cancer tissue microarray by in situ hybridization and found that miR-1247 was significantly down-regulated in pancreatic cancer tissues compared to matched benign tissues. High levels of miR-1247 expression were positively correlated with higher overall and recurrence free survival in pancreatic cancer patients, while negatively correlated with tumor grade. Using in vitro and in vivo models, we demonstrated that increased expression of miR-1247 inhibited proliferation, tumorigenicity, colony formation and triggered G0/G1 cell cycle arrest in pancreatic cancer cells. Moreover, we confirmed that neuropilin1 (NRP1) and neuropilin2 (NRP2) are direct targets of miR-1247 by western blot and luciferase reporter assay. Further studies indicated that low dose all trans retinoic acid (ATRA) can induce redifferentiation and restoration of miR-1247 in pancreatic cancer cells. These findings suggest that miR-1247, a novel tumor suppressor, can act as a potential biomarker and therapeutic agent 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 | 0.001 | 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) | 0.001 | 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".