Insulin and IGF-1, but not 17β-estradiol, alter the subcellular localization of MIER1α in MCF7 breast carcinoma cells
Bibliographic record
Abstract
BACKGROUND: MIER1α is a transcriptional regulator that interacts with estrogen receptor α and inhibits estrogen-stimulated growth of breast carcinoma cells. Interestingly, analysis of MIER1α subcellular localization in breast samples revealed a stepwise shift from the nucleus to the cytoplasm during progression to invasive carcinoma. Previously, we demonstrated that MIER1α is nuclear in MCF7 cells yet it does not contain a nuclear localization signal. Instead MIER1α is targeted to the nucleus through interaction and co-transport with HDAC 1 and 2. RESULTS: In this study, we demonstrate that treatment of MCF7 breast carcinoma cells with either insulin or insulin-like growth factor affects the subcellular localization of MIER1α. Both factors reduce the percentage of cells with nuclear MIER1α from 81 and 89 to 41 and 56%, respectively. Treatment with 17β-estradiol, on the other hand, had no effect and MIER1α remained nuclear. CONCLUSIONS: Our data demonstrate that insulin and IGF-1 can contribute to loss of nuclear MIER1α in the MCF7 breast carcinoma cell line.
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) | 0.002 | 0.001 |
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".