Y-Box Binding Protein-1 Induces the Expression of <i>CD44</i> and <i>CD49f</i> Leading to Enhanced Self-Renewal, Mammosphere Growth, and Drug Resistance
Post-publication record
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Bibliographic record
Abstract
Y-box binding protein-1 (YB-1) is an oncogenic transcription/translation factor expressed in >40% of breast cancers, where it is associated with poor prognosis, disease recurrence, and drug resistance. We questioned whether this may be linked to the ability of YB-1 to induce the expression of genes linked to cancer stem cells such as CD44 and CD49f. Herein, we report that YB-1 binds the CD44 and CD49f promoters to transcriptionally upregulate their expressions. The introduction of wild-type (WT) YB-1 or activated P-YB-1(S102) stimulated the production of CD44 and CD49f in MDA-MB-231 and SUM 149 breast cancer cell lines. YB-1-transfected cells also bound to the CD44 ligand hyaluronan more than the control cells. Similarly, YB-1 was induced in immortalized breast epithelial cells and upregulated CD44. Conversely, silencing YB-1 decreased CD44 expression as well as reporter activity in SUM 149 cells. In mice, expression of YB-1 in the mammary gland induces CD44 and CD49f with associated hyperplasia. Further, activated mutant YB-1(S102D) enhances self-renewal, primary and secondary mammosphere growth, and soft-agar colony growth, which were reversible via loss of CD44 or CD49f. We next addressed the consequence of this system on therapeutic responsiveness. Here, we show that paclitaxel induces P-YB-1(S102) expression, nuclear localization of activated YB-1, and CD44 expression. The overexpression of WT YB-1 promotes mammosphere growth in the presence of paclitaxel. Importantly, targeting YB-1 sensitized the CD44(High)/CD24(Low) cells to paclitaxel. In conclusion, YB-1 promotes cancer cell growth and drug resistance through its induction of CD44 and CD49f.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".