Corepressors LCoR and KAP1 form a complex with the transcription factor ZBRK1 to silence FGF2 gene expression and maintain breast cancer cell viability
Bibliographic record
Abstract
Through the use of a yeast two‐hybrid screen, we have a found a novel interaction between the C‐terminal domain of the transcriptional coregulator ligand‐dependent corepressor (LCoR) and the transcriptional corepressor Kruppel‐associated protein‐1 (KAP‐1). These transcriptional regulators associate with the transcription factor ZBRK1 and repress the expression of the Fibroblast Growth Factor 2 (FGF2) and Growth Arrest and DNA‐Damage‐inducible alpha (GADD45A) genes in breast cancer cell lines. Remarkably, ablation of ZBRK1, KAP‐1, or LCoR expression led to apoptotic death of specific breast cancer cells. This loss of viability could be partly rescued by simultaneous knockdown of FGF2, which abrogated the elevated FGF2 production in ZBRK1‐, KAP‐1, or LCoR‐deficient cells. Studies show that more malignant phenotypes in breast cancer are associated with loss of FGF2 expression, although the mechanism of this loss is poorly understood. We propose that ZBRK1, KAP‐1, and LCoR form a transcriptional complex that silences expression of genes, in particular FGF2, which enhances specific breast cancer cell viability. Research funded by the Canadian Institutes of Health Research (CIHR).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".