Retinol Inhibits the Invasion of Retinoic Acid–Resistant Colon Cancer Cells In Vitro and Decreases Matrix Metalloproteinase mRNA, Protein, and Activity Levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it