MicroRNA-137 reduces stemness features of pancreatic cancer cells by targeting KLF12
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cancer stem cells (CSCs) play an important role in the development of pancreatic cancer. We previously showed that the microRNA miR-137 is downregulated in clinical samples of pancreatic cancer, and its expression negatively regulates the proliferation and invasiveness of pancreatic cancer cells. METHODS: The stemness features of pancreatic cancer cells was detected by flow cytometry, immunofluorescence and sphere formation assay. Xenograft mouse models were used to assess the role of miR-137 in stemness features of pancreatic cancer cells in vivo. Dual-luciferase reporter assays were used to determine how miR-137 regulates KLF12. Bioinformatics and Chromatin immunoprecipitation analysis of KLF12 recruitment to the DVL2 promoters. Involvement of the Wnt/β-catenin pathways was investigated by western blot and Immunohistochemistry. RESULTS: miR-137 inhibits pancreatic cancer cell stemness in vitro and vivo. KLF12 as miR-137 target inhibits CSC phenotype in pancreatic cancer cells. Suppression of KLF12 by miR-137 inhibits Wnt/β-catenin signalling. KLF12 expression correlates with DVL2 and canonical Wnt pathway in clinical pancreatic cancer. CONCLUSION: Our results suggest that miR-137 reduces stemness features of pancreatic cancer cells by Targeting KLF12-associated Wnt/β-catenin pathways and may identify new diagnostic and therapeutic targets in pancreatic cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it