14-3-3ζ Orchestrates Mammary Tumor Onset and Progression via miR-221–Mediated Cell Proliferation
Bibliographic record
Abstract
14-3-3ζ is overexpressed in more than 40% of breast cancers, but its pathophysiologic relevance to tumorigenesis has not been established. Here, we show that 14-3-3ζ overexpression is sufficient to induce tumorigenesis in a transgenic mouse model of breast cancer. MMTV-LTR promoter-driven HA-14-3-3ζ transgenic mice (MMTV-HA-14-3-3ζ) developed mammary tumors, whereas control mice did not. Whey acidic protein promoter-driven HA-14-3-3ζ transgenic mice (WAP-HA-14-3-3ζ) developed hyperplastic lesions and showed increased susceptibility to carcinogen-induced tumorigenesis. When crossed with MMTV-neu transgenic mice, 14-3-3ζ.neu transgenic mice exhibited accelerated mammary tumorigenesis and metastasis compared with MMTV-neu mice. Mechanistically, 14-3-3ζ overexpression enhanced MAPK/c-Jun signaling, leading to increased miR-221 transcription, which inhibited p27 CDKI translation and, consequently, promoted cell proliferation. Importantly, this 14-3-3ζ-miR-221-p27 proliferation axis is also functioning in breast tumors in patients and is associated with high-grade cancers. Taken together, our findings show that overexpression of 14-3-3ζ has a causal role in mammary tumorigenesis and progression, acting through miR-221 in cooperation with known oncogenic events to drive neoplastic cell proliferation.
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) | 0.001 | 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".