RARβ involvement in enhancement of lung tumor cell immunogenicity revealed by array analysis
Bibliographic record
Abstract
The retinoid receptors (RARs and RXRs) are mediators of the multiple effects of retinoic acid. Of these, the retinoic acid receptor beta2 (RARbeta2) has frequently been shown to be the principal mediator of the growth and tumor suppressive effects of retinoic acid; this gene is inactivated in many epithelial tumors and their derived cell lines. We have searched for genes that are regulated by this isoform and are potentially involved in tumor suppression. Using the Atlas human cDNA array I, we identified 27 genes (not counting RARbeta itself) that are regulated, directly or indirectly, by RARbeta2 when it is transfected into Calu-1, a lung tumor-derived line that does not normally express RARbeta. Several of the affected genes code for proteins whose functions would augment the process of apoptosis and/or the host's immune response. The latter group included ICAM-1 and MHC class I heavy chain, whose protein products play particularly important roles in the mounting of an effective anti-tumor response. We then confirmed by flow cytometry that the observed increases in message levels were reflected in increased cell surface protein levels for ICAM-1 and MHC class I in RARbeta2 transfectants of two RARbeta-deficient lines, Calu-1 and the epidermoid lung cancer-derived line SK-MES. Finally, we showed that RARbeta2 transfection of Calu-1 cells enhanced the heterologous CTL response in both the induction and the effector phases by up to threefold. These results support the hypothesis that down-regulation of these genes (and possibly others) in RARbeta-deficient tumor cells contributes to immune system evasion, and suggest a novel therapeutic approach for this disease.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".