TGFβ/cyclin D1/Smad-mediated inhibition of BMP4 promotes breast cancer stem cell self-renewal activity
Bibliographic record
Abstract
Abstract Basal-like triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumor recurrence, and exhibit high resistance to drug treatments. The TNBC aggressive features are largely due to the high proportion of cancer stem cells present within these tumors. In this study, we investigated the interplay and networking pathways occurring between TGFβ family ligands in regulating stemness in TNBCs. We found that TGFβ stimulation of TNBCs resulted in enhanced tumorsphere formation efficiency and an increased proportion of the highly tumorigenic CD44high/CD24low cancer stem cell population. Analysis of the TGFβ transcriptome in TNBC cells revealed bone morphogenetic protein4 (BMP4) as a main TGFβ-repressed target in these tumor cells. We further found that BMP4 opposed TGFβ effects on stemness and potently decreased cancer stem cell numbers, thereby acting as a differentiation factor in TNBC. At the molecular level, we found that TGFβ inhibition of BMP4 gene expression is mediated through the Smad pathway and cyclin D1. In addition, we also found BMP4 to act as a pro-differentiation factor in normal mammary epithelial cells and promote mammary acinar formation in 3D cell culture assays. Finally, and consistent with our in vitro results, in silico patient data analysis defined BMP4 as a potential valuable prognosis marker for TNBC patients.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".