<b>Microrna 138 Upregulation is Associated with Decreasing Levels of <i>CCND1</i> Gene Expression and Promoting Cell Death in Human Prostate Cancer Cell Lines</b>
Bibliographic record
Abstract
Background: This research intended to discover the significance of miR-138 (microRNA 138) on the expression profile, proliferation, and the associated regulatory mechanisms in prostate cancer (PCa). Methods: Thirty-five specimens of prostate were studied to evaluate the expression level of miR138 by RT-qPCR (Quantitative reverse transcription polymerase chain reaction). Bioinformatics analysis was performed to search for the target genes of miR-138; and ABL1 (ABL proto-oncogene 1, non-receptor tyrosine kinase), CCND1 (cyclin D1), CCND3 (cyclin D3), VIM (vimentin), TWIST1 (twist family bHLH transcription factor 1), HIF1A (hypoxia-inducible factor 1 subunit alpha), and TERT (telomerase reverse transcriptase) genes were selected. Then, the biological role of miR-138 and CCND1 in the progression of PCa was investigated using RT-qPCR and luciferase reporter gene assay. Finally, overexpression of miR-138 on the proliferation in PCa cell lines was analyzed using the MTT (3-(4, 5-dimethylthiazol-2-Yl)-2, 5-diphenyltetrazolium bromide, Sigma, Germany) assay. Results: RT-qPCR showed that the expression of miR-138 downregulated in PCa tissues and cell lines. Bioinformatics analysis and RT-qPCR assay demonstrated that CCND1 expression level was negatively correlated with miR-138 in PCa tissues and the PC3 cell line. Moreover, CCND1 was predicted to be the target gene of miR138 in the PC3 cell line based on the results of luciferase reporter gene assay. Substantially, over-expression of miR138-5p mimic could inhibit the expression level of CCND1 gene in PC3 cell lines. Lastly, over-expression of miR-138 inhibited the proliferative capacities in PC3 and DU-145 cells. Conclusion: Our research introduces miR-138 as a negative regulator of CCND1 in the progression of PCa with an inhibitory impact on the proliferation rate of 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 | 0.001 |
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".