miR-105 Inhibits Prostate Tumour Growth by Suppressing CDK6 Levels
Bibliographic record
Abstract
A significant role for micro (mi)RNA in the regulation of gene expression in tumours has been recently established. In order to further understand how miRNA expression may contribute to prostate tumour growth and progression, we evaluated expression of miRNA in two invasive prostate tumour lines, PC3 and DU145, and compared it to that in normal prostate epithelial cells. Although a number of miRNAs were differentially expressed, we focused our analysis on miR-105, a novel miRNA not previously linked to prostate cancer. miR-105 levels were significantly decreased in both tumour cell lines in comparison to normal prostate epithelial cells. To determine its potential role in prostate cancer pathogenesis, we overexpressed miR-105 in both PC3 and DU145 cells and determined its effect on various tumourigenic properties. miR-105 overexpression inhibited tumour cell proliferation, tumour growth in anchorage-independent three-dimensional conditions and tumour invasion in vitro, properties of highly aggressive tumour cells. Of potential clinical significance, miR-105 overexpression inhibited tumour growth in vivo in xenograft models using these cell lines. We further identified CDK6 as a putative target of miR-105 which is likely a main contributor to the inhibition of tumour cell growth observed in our assays. Our results suggest that miR-105 inhibits tumour cell proliferation and hence may represent a novel therapeutically relevant cellular target to inhibit tumour growth or a marker of aggressive tumours in prostate cancer patients.
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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".