Differential regulation of protein expression, growth and apoptosis by natural and synthetic retinoids
Bibliographic record
Abstract
All-trans retinoic acid (ATRA) can down regulate the anti-apoptotic protein Bcl-2 and the cell cycle proteins cyclin D1 and cdk2 in estrogen receptor-positive breast cancer cells. We show here that retinoids can also reduce expression of the inhibitor of apoptosis protein, survivin. Here we have compared the regulation of these proteins in MCF-7 and ZR-75 breast cancer cells by natural and synthetic retinoids selective for the RA receptors (RARs) alpha, beta, and gamma then correlated these with growth inhibition, induction of apoptosis and chemosensitization to Taxol. In both cell lines ATRA and 9-cis RA induced the most profound decreases in cyclin D1 and cdk2 expression and also mediated the largest growth inhibition. The RARalpha agonist, Ro 40-6055 also strongly downregulated these proteins although did not produce an equivalent decrease in S-phase cells. Only ATRA induced RARbeta expression. ATRA, 9-cis RA and 4-HPR initiated the highest level of apoptosis as determined by mitochondrial Bax translocation, while only ATRA and 9-cis RA strongly reduced Bcl-2 and survivin protein expression. Enumeration of dead cells over 96 h correlated well with downregulation of both survivin and Bcl-2. Simultaneous retinoid-mediated reduction of both these proteins also predicted optimal Taxol sensitization. 4-HPR was much weaker than the natural retinoids with respect to Taxol sensitization, consistent with the proposed requirement for reduced Bcl-2 in this synergy. Neither the extent of cell cycle protein regulation nor AP-1 inhibition fully predicted the antiproliferative effect of the synthetic retinoids suggesting that growth inhibition requires regulation of a spectrum of RAR-regulated gene products in addition even to pivotal cell cycle proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".