LKB1 when associated with methylatedERα is a marker of bad prognosis in breast cancer
Bibliographic record
Abstract
Although the presence of nuclear estrogen receptor is widely used to guide breast cancer therapy, less attention has been paid to the receptor cytoplasmic signaling. Recently, we have shown that this pathway is operative in vivo and is activated in aggressive tumors representing a new potential target for breast cancer therapy. Here, we identified LKB1 as a partner of ERα and we explored its potential role in estrogen nongenomic signaling. The associations between LKB1 expression and the actors of this pathway, namely the methylated form of ERα (metERα), Src and PI3K, have been analyzed both in cultured cells and in 154 primary breast tumor samples. We found that LKB1 is a component of the cytoplasmic signaling complex in breast cell lines as well as in primary breast tumors. Moreover, an inverse correlation between the localization of LKB1 in nuclear and cytoplasmic compartments is observed. Importantly, high expression of cytoplasmic LKB1 is an independent marker of poor prognosis, associated with reduced overall survival (OS) and disease free survival (DFS). Conversely, the presence of nuclear LKB1 associates with increased OS and DFS. In conclusion, our results highlight that LKB1 expression in breast cancer appears to have opposite effects depending on its subcellular localization and may be used as a new prognostic biomarker.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".