DEAD-box protein p68 is regulated by β-catenin/transcription factor 4 to maintain a positive feedback loop in control of breast cancer progression
Bibliographic record
Abstract
INTRODUCTION: Nuclear accumulation of β-catenin is important for cancer development and it is found to overlap with p68 (DDX5) immunoreactivity in most breast cancers, as indicated by both clinical investigations and studies in cell lines. In this study, we aim to investigate the regulation of p68 gene expression through β-catenin/transcription factor 4 (TCF4) signaling in breast cancer. METHODS: Formalin-fixed paraffin-embedded sections derived from normal human breast and breast cancer samples were used for immunohistochemical analysis. Protein and mRNA expressions were determined by immunoblotting and quantitative RT-PCR respectively. Promoter activity of p68 was checked using luciferase assay. Occupancy of several factors on the p68 promoter was evaluated using chromatin immunoprecipitation. Finally, a syngeneic mouse model of breast cancer was used to assess physiological significance. RESULTS: We demonstrated that β-catenin can directly induce transcription of p68 promoter or indirectly through regulation of c-Myc in both human and mouse breast cancer cells. Moreover, by chromatin immunoprecipitation assay, we have found that both β-catenin and TCF4 occupy the endogenous p68 promoter, which is further enhanced by Wnt signaling. Furthermore, we have also established a positive feedback regulation for the expression of TCF4 by p68. To the best of our knowledge, this is the first report on β-catenin/TCF4-mediated p68 gene regulation, which plays an important role in epithelial to mesenchymal transition, as shown in vitro in breast cancer cell lines and in vivo in an animal breast tumour model. CONCLUSIONS: Our findings indicate that Wnt/β-catenin signaling plays an important role in breast cancer progression through p68 upregulation.
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) | 0.002 | 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".