Calcitriol increases Dicer expression and modifies the microRNAs signature in SiHa cervical cancer cells
Bibliographic record
Abstract
MicroRNAs play important roles in cancer biology. Calcitriol, the hormonal form of vitamin D3, regulates microRNAs expression in tumor cells. In the present study we asked if calcitriol would modify some of the components of the microRNA processing machinery, namely, Drosha and Dicer, in calcitriol-responsive cervical cancer cells. We found that calcitriol treatment did not affect Drosha mRNA; however, it significantly increased Dicer mRNA and protein expression in VDR-positive SiHa and HeLa cells. In VDR-negative C33-A cells, calcitriol had no effect on Dicer mRNA. We also found a vitamin D response element in Dicer promoter that interacts in vitro to vitamin D and retinoid X receptors. To explore the biological plausibility of these results, we asked if calcitriol alters the microRNA expression profile in SiHa cells. Our results revealed that calcitriol regulates the expression of a subset of microRNAs with potential regulatory functions in cancer pathways, such as miR-22, miR-296-3p, and miR-498, which exert tumor-suppressive effects. In summary, the data indicate that in SiHa cells, calcitriol stimulates the expression of Dicer possibly through the vitamin D response element located in its promoter. This may explain the calcitriol-dependent modulation of microRNAs whose target mRNAs are related to anticancer pathways, further adding to the various anticancer mechanisms of calcitriol.
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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".