MicroRNA<i>miR-17-5p</i>is overexpressed in pancreatic cancer, associated with a poor prognosis, and involved in cancer cell proliferation and invasion
Bibliographic record
Abstract
The microRNA-17-92 cluster is an oncogene in human B cell lymphomas and lung cancers. Previous microRNA microarray data revealed that miR-17-5p, a member of the miR-17-92 cluster, is upregulated in pancreatic cancer. However, the involvement of miR-17-5p expression in pancreatic carcinogenesis has not well been studied. In the present study, we measured the miR-17-5p expression levels in pancreatic cancer cell lines, primary cultures of normal human pancreatic ductal cells, formalin-fixed paraffin-embedded (FFPE) tissue samples derived from 80 patients who underwent pancreatectomy for pancreatic cancer and microdissected cells (including normal ductal epithelial, pancreatic intraepithelial neoplasia-1B and invasive ductal carcinoma cells) by qRT-PCR. Furthermore, we investigated the effects of upregulation of miR-17-5p expression on the proliferation and invasion of pancreatic cancer cells. We found that pancreatic cancer cells expressed higher levels of miR-17-5p than primary cultured normal ductal cells. miR-17-5p was also overexpressed in pancreatic cancer in FFPE and microdissected samples. Furthermore, analysis of macrodissected FFPE samples revealed that high miR-17-5p expression was associated with a poor prognosis (p = 0.03). In addition, in vitro experiments revealed that SUIT-2 and KP-2 pancreatic cancer cells transfected with the miR-17-5p precursor showed significantly higher cell growth ratios than the corresponding control cells (p < 0.001 and p = 0.012, respectively), as well as significantly higher numbers of invading cells (p < 0.0001 for both). The present findings suggest that miR-17-5p plays important roles in pancreatic carcinogenesis and cancer progression, and is associated with a poor prognosis in 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".