Identification of the <i>miR-106b</i> ~ <i>25</i> MicroRNA Cluster as a Proto-Oncogenic <i>PTEN</i> -Targeting Intron That Cooperates with Its Host Gene <i>MCM7</i> in Transformation
Bibliographic record
Abstract
PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tumor suppressor that antagonizes signaling through the phosphatidylinositol 3-kinase-Akt pathway. We have demonstrated that subtle decreases in PTEN abundance can have critical consequences for tumorigenesis. Here, we used a computational approach to identify miR-22, miR-25, and miR-302 as three PTEN-targeting microRNA (miRNA) families found within nine genomic loci. We showed that miR-22 and the miR-106b~25 cluster are aberrantly overexpressed in human prostate cancer, correlate with abundance of the miRNA processing enzyme DICER, and potentiate cellular transformation both in vitro and in vivo. We demonstrated that the intronic miR-106b~25 cluster cooperates with its host gene MCM7 in cellular transformation both in vitro and in vivo, so that the concomitant overexpression of MCM7 and the miRNA cluster triggers prostatic intraepithelial neoplasia in transgenic mice. Therefore, the MCM7 gene locus delivers two simultaneous oncogenic insults when amplified or overexpressed in human cancer. Thus, we have uncovered a proto-oncogenic miRNA-dependent network for PTEN regulation and defined the MCM7 locus as a critical factor in initiating prostate tumorigenesis.
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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) | 0.000 | 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 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".