MicroRNA 138 upregulation is associated with decreasing levels of CCND1 gene expression and promoting cell death in human prostate cancer cell lines
Bibliographic record
Abstract
This research intended to discover the significance of miR-138 on the espression profile, proliferation, and the associated regulatory mechanisms in prostate cancer (PCa). RT-qPCR was applied to compare the expression of miR-138 in the PCa cells with a non-cancer cell line, as well as PCa tissue samples with benign prostatic hyperplasia (BPH) samples. The expression of miR-138 notably diminished in PCa tissues and cell lines. Afterward, formerly documented genes, along with bioinformatics analysis, suggested seven possible target genes of miR-138. Among them, CCND1 seemed to have higher expression in the PCa cell lines and tissues. Also, the negative correlation of miR-138 and CCND1 in PCa cell line and tissues was validated using Pearson correlation. CCDN1 was revealed to be the target gene of miR138 in the PC3 cell line based on the results of the luciferase reporter gene assay. Over-expression of miR138-5p suppressed the expression of CCDN1 in PCa cell lines as exhibited by RT-qPCR. Finally, the results of the MTT assay exhibited the inhibitory impact of miR-138 on the proliferative capacities in PCa cell lines. Our research introduces miR-138 as a negative regulator of CCDN1 in the progression of PCa with an inhibitory impact on the proliferation rate of prostate cancer (PCa) cell lines. This regulatory mechanism could be utilized for the design and target selection of remedial miRNA-based approaches.
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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) | 0.002 | 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".