miR-203 Regulates Cell Proliferation through Its Influence on Hakai Expression
Bibliographic record
Abstract
Gene expression is potently regulated through the action of microRNAs (miRNAs). Here, we present evidence of a miRNA regulating Hakai protein. Hakai was discovered as an E3 ubiquitin-ligase that mediates the posttranslational downregulation of E-cadherin, a major component of adherens junctions in epithelial cells and a potent tumour suppressor. Recent data have provided evidence that Hakai affects cell proliferation in an E-cadherin-independent manner, thus revealing a role for Hakai in the early stages of tumour progression. Furthermore, Hakai is highly up-regulated in human colon adenocarcinomas compared to normal tissues. However, the molecular mechanisms that regulate Hakai abundance are unknown. We identified two putative sites of miR-203 interaction on the Hakai mRNA, in its 3'-untranslated region (UTR). In several human carcinoma cell lines tested, overexpression of a miR-203 precursor (Pre-miR-203) reduced Hakai abundance, while inhibiting miR-203 by using an antisense RNA (Anti-miR-203) elevated Hakai levels. The repressive influence of miR-203 on the Hakai 3'-UTR was confirmed using heterologous reporter constructs. In keeping with Hakai's proliferative influence, Anti-miR-203 significantly increased cell number and BrdU incorporation, while Pre-miR-203 reduced these parameters. Importantly, the growth-promoting effects of anti-miR-203 required the presence of Hakai, because downregulation of Hakai by siRNA suppressed its proliferative action. Finally, in situ hybridization showed that miR-203 expression is attenuated in colon tumour tissues compared to normal colon tissues, suggesting that miR-203 could be a potential new prognostic marker and therapeutic target to explore in colon cancer. In conclusion, our findings reveal, for the first time, a post-transcriptional regulator of Hakai expression. Furthermore, by lowering Hakai abundance, miR-203 also reduces Hakai-regulated-cell division.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".