A resource for analysis of microRNA expression and function in pancreatic ductal adenocarcinoma cells
Bibliographic record
Abstract
MicroRNAs (miRNAs) are 21-24 nucleotide RNA molecules that regulate the translation and stability of target messenger RNAs. Abnormal miRNA expression is a common feature of diverse cancers. Several previous studies have classified miRNA expression in pancreatic ductal adenocarcinoma (PDAC), although no uniform pattern of miRNA dysregulation has emerged. To clarify these previous findings as well as to set the stage for detailed functional analyses, we performed global miRNA expression profiling of 21 human PDAC cell lines, the most extensive panel studied to date. Overall, 39 miRNAs were found to be dysregulated and have at least two-fold or greater differential expression in PDAC cell lines compared to control nontransformed pancreatic ductal cell lines. Several of these miRNAs show comparable dysregulation in first-passage patient derived xenografts. Initial functional analyses demonstrate that enforced expression of miRNAs derived from the miR-200 family and the miR-17-92 cluster, both of which are overexpressed in PDAC cell lines, enhances proliferation. In contrast, inhibition of the miR-200 family, the miR-17-92 cluster, or miR-191 diminishes anchorage independent growth. Consistent with a known role for the miR-200 family in negatively regulating an epithelial-to-mesenchymal transition (EMT), the abundance of these miRNAs correlated positively with E-cadherin expression and negatively with the EMT-associated transcription factor and established miR-200 target ZEB1. Finally, restituted expression of miR-34a, a miRNA whose expression is frequently lost in PDAC cell lines, abrogates growth, demonstrating that the anti-proliferative activity of this miRNA is operative in PDAC. These results, and the widespread availability of PDAC cell lines wherein the aforementioned data were generated, provide a valuable resource for the pancreatic cancer research community and will greatly facilitate functional studies essential for elucidating the consequences of miRNA dysregulation 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.078 |
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".