STAT-5 and STAT-6 in Breast Cancer: Potential Crosstalk With Estrogen and Progesterone Receptors Can Affect Cell Proliferation and Metastasis
Bibliographic record
Abstract
Background: Signal transducers and activators of transcription 5a and 6 (STAT5a and STAT6) play a critical role in tumorigenesis of mammary glands. Based on previous studies, the breast cancer is largely dependent on hormone receptors. Consequently, it is very interesting to decipher the relationship between the STAT5a and STAT6 expression and the molecular distribution of estrogen receptors (ERs) and progesterone receptors (PRs) in mammary tumors. Methods: Our study analyzed the expression of STAT5a and STAT6, ERα, ERβ and PR in 40 breast tumor tissues using quantitative real-time polymerase chain reaction (qRT-PCR). Furthermore, the Ki-67 and HER2 status were detected using immunohistochemistry. Results: STAT5a and STAT6 were retained in the majority of the cases studied. Increasing of STAT5a and STAT6 is significantly associated with ERs and PR. The coexpression of both STAT5a and STAT6 with ERs and PR is associated with high tumor grades. Moreover, the coexpression of STAT5a and STAT6 with ERα and PR is associated with a high proliferation index. In addition, (STAT6 + ERβ+) and (STAT6 + PR+) breast cancer subgroups are associated with lymph node infiltration (P = 0.001 and P = 0.03, respectively). Conclusions: Our study results provide an interaction between STAT5a and STAT6 with ERs and PR inducing cell proliferation. Coexpression of STAT5a and STAT6 with ERs and PR can predict sensibility to hormonal therapy.
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) | 0.003 | 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".