MicroRNA-153 is a prognostic marker and inhibits cell migration and invasion by targeting SNAI1 in human pancreatic ductal adenocarcinoma
Post-publication record
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Bibliographic record
Abstract
Human pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with early metastasis, which leads to poor prognosis for patients. Mounting evidence suggests that microRNAs (miRNAs) act as critical factors for tumor recurrence and metastasis. miR-153 has been suggested as a novel tumor-associated miRNA, which is involved in tumor metastasis. However, the clinical significance of miR-153 and its role in PDAC remains to be investigated. The aim of the present study was to investigate the expression levels of miR-153 using RT-qPCR in human PDAC cell lines and tissues. A clinical association analysis was performed to investigate the clinical significance of miR-153. The results showed that, the relative expression of miR-153 in PDAC cells was obviously decreased as compared to that in the normal human pancreatic duct epithelial cell line. The mean expression of miR-153 in PDAC tissues was significantly reduced as compared to that in the normal pancreatic tissues. The clinical analysis revealed that a low expression of miR-153 was closely associated with poor prognostic features and shorter long-term survival of PDAC patients. Furthermore, univariate and multivariate Cox regression analyses showed that miR-153 was an independent prognostic factor for predicting survival in PDAC patients. In vitro studies demonstrated that the upregulation of miR-153 inhibited migration and invasion in MIAPaCa-2 cells. By contrast, the downregulation of miR-153 increased the number of migrated and invaded AsPC-1 cells. miR-153 inversely regulated SNAI1 abundance in MIAPaCa-2 cells. Notably, SNAI1 was identified as a direct target of miR-153 in PDAC. Furthermore, an inverse correlation between miR-153 and SNAI1 expression was observed in PDAC tissues. In conclusion, the results showed miR-153 is an independent prognostic marker for predicting survival in PDAC patients and inhibits cell migration and invasion by targeting SNAI1.
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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) | 0.001 | 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 | 0.001 |
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".