Periostin gene expression in neu-positive breast cancer cells is regulated by a FGFR signaling cross talk with TGFβ/PI3K/AKT pathways
Bibliographic record
Abstract
BACKGROUND: Breast cancer is a highly heterogeneous disease with multiple drivers and complex regulatory networks. Periostin (Postn) is a matricellular protein involved in a plethora of cancer types and other diseases. Postn has been shown to be involved in various processes of tumor development, such as angiogenesis, invasion, cell survival and metastasis. The expression of Postn in breast cancer cells has been correlated with a more aggressive phenotype. Despite extensive research, it remains unclear how epithelial cancer cells regulate Postn expression. METHODS: Using murine tumor models and human TMAs, we have assessed the proportion of tumor samples that have acquired Postn expression in tumor cells. Using biochemical approaches and tumor cell lines derived from Neu+ murine primary tumors, we have identified major regulators of Postn gene expression in breast cancer cell lines. RESULTS: Here, we show that, while the stromal compartment typically always expresses Postn, about 50% of breast tumors acquire Postn expression in the epithelial tumor cells. Furthermore, using an in vitro model, we show a cross-regulation between FGFR, TGFβ and PI3K/AKT pathways to regulate Postn expression. In HER2-positive murine breast cancer cells, we found that basic FGF can repress Postn expression through a PKC-dependent pathway, while TGFβ can induce Postn expression in a SMAD-independent manner. Postn induction following the removal of the FGF-suppressive signal is dependent on PI3K/AKT signaling. CONCLUSION: Overall, these results reveal a novel regulatory mechanism and shed light on how breast tumor cells acquire Postn expression. This complex regulation is likely to be cell type and cancer specific as well as have important therapeutic implications.
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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".