miR-192, miR-194 and miR-215: a convergent microRNA network suppressing tumor progression in renal cell carcinoma
Bibliographic record
Abstract
MicroRNAs (miRNAs) play a crucial role in tumor progression and metastasis. We, and others, recently identified a number of miRNAs that are dysregulated in metastatic renal cell carcinoma compared with primary renal cell carcinoma. Here, we investigated three miRNAs that are significantly downregulated in metastatic tumors: miR-192, miR-194 and miR-215. Gain-of-function analyses showed that restoration of their expression decreases cell migration and invasion in renal cell carcinoma cell line models, whereas knockdown of these miRNAs resulted in enhancing cellular migration and invasion abilities. We identified three targets of these miRNAs with potential role in tumor aggressiveness: murine double minute 2, thymidylate synthase, and Smad Interacting protein 1/zinc finger E-box binding homeobox 2. We observed a convergent effect (the same molecule can be targeted by all three miRNAs) and a divergent effect (the same miRNA can control multiple targets) for these miRNAs. We experimentally validated these miRNA-target interactions using three independent approaches. First, we observed that miRNA overexpression significantly reduces the mRNA and protein levels of their targets. In the second, we observed significant reduction of the luciferase signal of a vector containing the 3'UTR of the target upon miRNA overexpression. Finally, we show the presence of inverse correlation between miRNA changes and the expression levels of their targets in patient specimens. We also examined the prognostic significance of miR-215 in renal cell carcinoma. Lower expression of miR-215 is associated with significantly reduced disease-free survival time. These findings were validated on an independent data set from The Cancer Genome Atlas. These results can pave the way to the clinical use of miRNAs as prognostic markers and therapeutic targets.
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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".