Retinol Inhibits the Invasion of Retinoic Acid–Resistant Colon Cancer Cells In Vitro and Decreases Matrix Metalloproteinase mRNA, Protein, and Activity Levels
Bibliographic record
Abstract
Retinol inhibits the growth of all-trans-retinoic acid (ATRA)-resistant human colon cancer cell lines through a retinoic acid receptor (RAR)-independent mechanism. The objectives of the current study were to determine if retinol inhibited the invasion of ATRA-resistant colon cancer cells independent of RAR and the effects of retinol on matrix metalloproteinases (MMPs). Retinol inhibited the migration and invasion of two ATRA-resistant colon cancer cell lines, HCT-116 and SW620, in a dose-dependent manner. To determine if transcription, particularly RAR-mediated transcription, or translation of new genes was required for retinol to inhibit cell invasion, cells were treated with retinol and cycloheximide, actinomycin D, or an RAR pan-antagonist. Treatment of cells with retinol and cycloheximide, actinomycin D, or an RAR pan-antagonist did not block the ability of retinol to inhibit cell invasion. In addition, retinol decreased MMP-1 mRNA levels in both cell lines, MMP-2 mRNA levels in the SW620 cell line, and MMP-7 and -9 mRNA levels in the HCT-116 cell line. Retinol also decreased the activity of MMP-2 and -9 and MMP-9 protein levels while increasing tissue inhibitor of MMP-1 media levels. In conclusion, retinol reduces the metastatic potential of ATRA-resistant colon cancer cells via a novel RAR-independent mechanism that may involve decreased MMP mRNA levels and activity.
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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 | 0.001 |
| 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".