Rho<scp>GDI</scp>β promotes Sp1/<scp>MMP</scp>‐2 expression and bladder cancer invasion through perturbing miR‐200c‐targeted <scp>JNK</scp>2 protein translation
Bibliographic record
Abstract
Our most recent studies demonstrate that RhoGDIβ is able to promote human bladder cancer (BC) invasion and metastasis in an X-link inhibitor of apoptosis protein-dependent fashion accompanied by increased levels of matrix metalloproteinase (MMP)-2 protein expression. We also found that RhoGDIβ and MMP-2 protein expressions are consistently upregulated in both invasive BC tissues and cell lines. In the present study, we show that knockdown of RhoGDIβ inhibited MMP-2 protein expression accompanied by a reduction of invasion in human BC cells, whereas ectopic expression of RhoGDIβ upregulated MMP-2 protein expression and promoted invasion as well. The mechanistic studies indicated that MMP-2 was upregulated by RhoGDIβ at the transcriptional level by increased specific binding of the transcription factor Sp1 to the mmp-2 promoter region. Further investigation revealed that RhoGDIβ overexpression led to downregulation of miR-200c, whereas miR-200c was able directly to target 3'-UTR of jnk2mRNA and attenuated JNK2 protein translation, which resulted in attenuation of Sp1mRNA and protein expression in turn, inhibiting Sp1-dependent mmp-2 transcription. Collectively, our studies demonstrate that RhoGDIβ overexpression inhibits miR-200c abundance, which consequently results in increases of JNK2 protein translation, Sp1 expression, mmp-2 transcription, and BC invasion. These findings, together with our previous results showing X-link inhibitor of apoptosis protein mediating mRNA stabilization of both RhoGDIβ and mmp-2, reveal the nature of the MMP-2 regulatory network, which leads to MMP-2 overexpression and BC invasion.
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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 | 0.001 |
| 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 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".