Abstract 3067: Regulation of PAX2 expression/activity by hypoxia and EGF in breast cancer cells
Bibliographic record
Abstract
Abstract Breast cancer is the most common cancer in women and is associated with the highest mortality rate after lung cancer. The leading cause of deaths associated with this cancer is metastasis, but cellular and molecular mechanisms controlling the metastatic ability of breast cancer cells are only partially elucidated. There is direct and indirect evidence suggesting that PAX2, a transcription factor preferentially activated in breast cancer cells of the luminal subtype, could reduce the invasive and metastatic ability of these cells, by downregulating the expression of ERBB2. It is thus primordial to understand the mechanisms regulating the expression and activity of PAX2 in breast cancer cells. Specifically, because hypoxic conditions prevailing in the center of solid primary tumours are known to regulate the expression of many genes controlling invasion and metastasis, we are investigating the impact of hypoxia-mimicking agent cobalt chloride (CoCl2) on PAX2 expression in breast cancer cells. Similarly, because EGF growth factor regulates the expression and/or activity of many genes controlling invasion and metastasis, we are determining the impact of EGF exposure on PAX2 activation and/or gene expression in ER+ MCF-7 cell line. The identification of factors and mechanisms regulating the expression and activity of PAX2 in breast cancer cells will help understand the biology of this emerging factor in breast cancer cells and may provide multiple strategies to control invasion and metastasis of these cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3067. doi:1538-7445.AM2012-3067
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) | 0.004 | 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".