Transcriptional Regulation of miR-31 by Oncogenic KRAS Mediates Metastatic Phenotypes by Repressing RASA1
Bibliographic record
Abstract
UNLABELLED: Activating KRAS mutations are nearly ubiquitous in pancreatic cancer occurring in more than 95% of clinical cases. miRNAs are small noncoding RNAs that regulate gene expression by binding sequences within the 3'UTRs of target mRNAs. An integral role for miRNAs in cancer pathogenesis is well established; however, the role of miRNAs in KRAS-mediated tumorigenesis is poorly characterized. Here it is demonstrated that expression of miR-31 is coupled to the expression of oncogenic KRAS and activity of the MAPK pathway. miR-31 is highly expressed in patient-derived xenografts and a panel of pancreatic and colorectal cancer cells harboring activating KRAS mutations. The miR-31 host gene is a large noncoding RNA that correlates with miR-31 expression and enabled identification of the putative miR-31 promoter. Using luciferase reporters, a minimal RAS-responsive miR-31 promoter was found to drive robust luciferase activity dependent on expression of mutant KRAS and the transcription factor ELK1. Furthermore, ELK1 interacts directly with the endogenous miR-31 promoter in a MAPK-dependent manner. Expression of enforced miR-31 significantly enhanced invasion and migration of multiple pancreatic cancer cells resulting from the activation of RhoA through regulation of the miR-31 target gene RASA1. Importantly, acute knockdown of RASA1 phenocopied enforced miR-31 expression on the migratory behavior of pancreatic cancer cells through increased RhoA activation. IMPLICATIONS: Oncogenic KRAS can activate Rho through the miR-31-mediated regulation of RASA1 indicating miR-31 acts as a KRAS effector to modulate invasion and migration 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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".