<i>let-7</i> MicroRNA Transfer in Pancreatic Cancer-Derived Cells Inhibits <i>In Vitro</i> Cell Proliferation but Fails to Alter Tumor Progression
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is still the fourth leading cause of cancer-related deaths in Western countries, with increasing incidence. Neither effective prognostic markers nor therapies exist for this cancer. MicroRNAs are potent inhibitors of protein translation, and aberrantly expressed in many cancers. Because let-7 microRNA targets the K-ras oncogene, we aimed to characterize let-7 expression and function in PDAC in vitro and in vivo. Let-7 expression was quantified by real-time RT-PCR from resected tumors and matching adjacent tissue, and in endoscopic ultrasound-guided fine needle aspiration material from patients with PDAC. Let-7 is detected by reverse transcription in situ PCR in a PDAC tissue microarray. PDAC-derived cells were transfected with plasmid-based synthetic microRNAs or by lentiviral transduction, in vitro and in vivo. Let-7 microRNA expression is strongly reduced in PDAC samples, as compared with adjacent tissue. Let-7 is present in normal acinar pancreatic cells, and lost in poorly differentiated cancer samples. In addition, let-7 expression was repressed in patients with PDAC not eligible for surgery. Restoring let-7 levels in cancer-derived cell lines strongly inhibits cell proliferation, K-ras expression, and mitogen-activated protein kinase activation, but fails to impede tumor growth progression after intratumoral gene transfer or after implantation of Capan-1 cells stably overexpressing let-7 microRNA. We describe here for the first time the extensive loss of expression of let-7 in PDAC. In addition, this study provides the initial steps for a microRNA replacement therapy for this 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 | 0.001 | 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".