MicroRNA-188 suppresses G1/S transition by targeting multiple cyclin/CDK complexes
Bibliographic record
Abstract
BACKGROUND: Accelerated cell cycle progression is the common feature of most cancers. MiRNAs can act as oncogenes or tumor suppressors by directly modulating cell cycle machinery. It has been shown that miR-188 is upregulated in UVB-irradiated mouse skin and human nasopharyngeal carcinoma CNE cells under hypoxic stress. However, little is known about the function of miR-188 in cell proliferation and growth control. RESULTS: Overexpression of miR-188 inhibits cell proliferation, tumor colony formation and G1/S cell cycle transition in human nasopharyngeal carcinoma CNE cells. Using bioinformatics approach, we identify a series of genes regulating G1/S transition as putative miR-188 targets. MiR-188 inhibits both mRNA and protein expression of CCND1, CCND3, CCNE1, CCNA2, CDK4 and CDK2, suppresses Rb phosphorylation and downregulates E2F transcriptional activity. The expression level of miR-188 also inversely correlates with the expression of miR-188 targets in human nasopharyngeal carcinoma (NPC) tissues. Moreover, studies in xenograft mouse model reveal that miR-188 is capable of inhibiting tumor initiation and progression by suppressing target genes expression and Rb phosphorylation. CONCLUSIONS: This study demonstrates that miR-188 exerts anticancer effects, via downregulation of multiple G1/S related cyclin/CDKs and Rb/E2F signaling pathway.
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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".