Regulation of TGFÃ signaling on tumor cell migration, invasion and stem cell activity in triple negative breast cancer
Bibliographic record
Abstract
Basal-like triple negative breast cancers (TNBCs) display poor prognostic features with larger tumor size, higher tumor grade, and an increased risk for lymph node and distant metastasis as well as tumor recurrence. Transforming growth factor beta (TGFβ) is a key regulator of the cellular processes by which breast cancer cells from the primary tumor metastasize to distant organs. However, the molecular mechanisms underlying TGFβ's pro-metastatic effects remain to be fully elucidated. Here, we investigated the role of TGFβ signaling pathway in regulating cell migration, invasion and cancer stem cell self-renewal capacity, which are the initial and critical steps in breast cancer metastasis. Our studies initially identified a novel function for the cell cycle regulator p21 and its binding partner acetyltransferase p/CAF as critical transcriptional regulators of TGFβ-induced TNBC cell migration and invasion in vitro as well as tumor invasiveness in vivo. As p21 can interact with different cyclin and CDK complexes, we investigated whether other cell cycle regulators are also involved in TGFβ-induced tumor progression. We found that TGFβ promotes physical interaction and nuclear co-localization between cyclin D1 and p21. The co-expression of cyclin D1 and p21 proteins promote tumor growth and locally invasive tumors. In addition, we found that TGFβ can activate cyclin D1/CDK4 complex to promote cancer stem cell activity and self-renewal capacity in TNBC. Together, we have defined p21, cyclin D1 and CDK4 as key downstream regulators of TGFβ tumor-promoting functions.
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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) | 0.001 | 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".