Knockdown of <i>RARB2</i> identifies a dual role in cancer
Bibliographic record
Abstract
Two chemoprevention trials have shown that retinoic acid (RA) may be harmful in patients at risk for lung cancer, and RA administration to this high-risk group results in RARB2 reactivation. Although RARB2 is thought to possess tumor suppressive activity, its expression has recently been correlated with poorer prognosis in patients with nonsmall cell lung cancer. We hypothesized that RARB2 expression is necessary for the growth and maintenance of the oncogenic phenotype in lung cancer cells in which RARB2 has not been inactivated. We tested various antisense oligodeoxynucleotides (ASO) against RARB2 in multiple lung cancer cell lines and used microarray technology to compare the patterns of gene expression following ASO treatment versus RA treatment in the A-549 lung cancer cell line. We show that ASO treatment reduces proliferation and causes apoptosis in 3 RARB2-expressing lung cancer cell lines but has no apparent effect in at least two other lung cancer cells lines having lost RARB2 expression or one normal lung RARB2-expressing cell line; we demonstrate a correlation between resulting RARB2 expression levels and cell growth; and identify transcriptional effects related to both RA and RARB2 signaling. In particular, five genes known to contribute to carcinogenesis or chemotherapeutic resistance are down-regulated following ASO treatment: three of these are up-regulated following RA treatment. This work demonstrates a dual role for RARB2 (tumor suppression and tumor promotion) and identifies a challenge with respect to using RARB2 as a target for treatment or prevention strategies.
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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 | 0.001 | 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".