miR-204 targets Bcl-2 expression and enhances responsiveness of gastric cancer
Bibliographic record
Abstract
Micro RNAs (miRs) are small non-coding RNAs aberrantly expressed in human tumors. Here, we aim to identify miRs whose deregulated expression leads to the activation of oncogenic pathways in human gastric cancers (GCs). Thirty nine out of 123 tumoral and matched uninvolved peritumoral gastric specimens from three independent European subsets of patients were analyzed for the expression of 851 human miRs using Agilent Platform. The remaining 84 samples were used to validate miRs differentially expressed between tumoral and matched peritumoral specimens by qPCR. miR-204 falls into a group of eight miRs differentially expressed between tumoral and peritumoral samples. Downregulation of miR-204 has prognostic value and correlates with increased staining of Bcl-2 protein in tumoral specimens. Ectopic expression of miR-204 inhibited colony forming ability, migration and tumor engraftment of GC cells. miR-204 targeted Bcl-2 messenger RNA and increased responsiveness of GC cells to 5-fluorouracil and oxaliplatin treatment. Ectopic expression of Bcl-2 protein counteracted miR-204 pro-apoptotic activity in response to 5-fluorouracil. Altogether, these findings suggest that modulation of aberrant expression of miR-204, which in turn releases oncogenic Bcl-2 protein activity might hold promise for preventive and therapeutic strategies of GC.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".