Caveolin-1 expression is elevated in claudin-low mammary tumor cells
Bibliographic record
Abstract
BACKGROUND: Caveolin-1 is a scaffolding protein found in plasma membrane invaginations known as caveolae. Caveolin-1 can regulate a number of intracellular processes such as signal transduction, cholesterol metabolism and vesicular transport. With respect to breast cancer caveolin-1 has been observed in both tumor cells and stromal cells surrounding tumors however most of the recent research has focused on how the loss of caveolin-1 in the stromal cells surrounding the tumor alters the tumor microenvironment. METHODS: Caveolin-1 expression was evaluated in (1) mammary tumors induced by the transgenic overexpression of the type I insulin-like growth factor receptor (IGF-IR), (2) mammary tumors that became independent of IGF-IR signalling and acquired a claudin-low genotype, (3) two murine mammary epithelial tumor cell lines and (4) two murine mammary claudin-low tumor cell lines. RESULTS: We found that mammary tumors induced by IGF-IR overexpression expressed low levels of caveolin-1 while mammary tumors that became independent of IGF-IR signalling expressed considerably higher levels of caveolin-1. Interestingly, pockets of caveolin-1 positive cells could be observed in some of the IGF-IR-induced mammary tumors and these caveolin-1 positive cells were associated with tumor cells that expressed basal cytokeratins (cytokeratins 5 and 14). This caveolin-1 expression pattern was maintained in the murine mammary tumor cell lines in that the epithelial mammary tumor cell lines expressed little or no caveolin-1 while the claudin-low cell lines expressed caveolin-1. CONCLUSIONS: Our model indicates that mammary tumor cells with epithelial characteristics lack caveolin-1 while mesenchymal tumor cells express caveolin-1 suggesting that caveolin-1 may serve as a marker of mammary tumor cells with mesenchymal characteristics such as claudin-low breast tumors.
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".