Aberrant STAT3 Ser727 phosphorylation and Its association with negative estrogen receptor status in breast infiltrating ductal carcinoma
Bibliographic record
Abstract
3699 Purpose: Phosphorylation in serine residue 727 is able to modulate the transcriptional activity of STAT3. However, the role of STAT3 serine phosphorylation in breast cancer is mostly unexplored. In this study, we analyzed the expression patterns of serine residue 727-phosphorylated STAT3 (p-ser727-STAT3) in breast cancer and correlated its expression profiles with clinicopathological parameters. Experimental Design: Immunoblotting and immunohistochemistry techniques, using antibodies against p-ser727-STAT3 and STAT3, respectively, were applied on 48 pairs of breast infiltrating ductal carcinoma tissues and its nearby noncancer breast tissues. The results were then analyzed for their significance by multivariate analyses. Results : Significantly elevated p-ser727-STAT3 expression, after normalization by total STAT3 protein, was observed in 60.4 % (29/48) of the breast cancer tissues, in comparison to the matched noncancer breast tissues ( P P = 0.027, 0.031, and 0.005 individually). Intriguingly, we noticed that the expression levels of p-ser727-STAT3 in ER-negative breast cancer cell lines (MDA-MB-231, SKBR-3 and MDA-MB-468) were higher than that in ER-positive breast cancer cell lines (MCF-7, ZR75-1 and T47D) and the immortalized breast epithelial cell line (MCF-10A). On the other hand, treatment with ERα-specific siRNA or anti-breast cancer drug tamoxifen increased the expression of p-ser727-STAT3 in MCF-7 cells, but not ER-negative MDA-MB-231 cells, in a dose-dependent manner. Conclusions: Our results suggested a possible involvement of p-ser727-STAT3 in the pathogenesis of breast cancer. Besides, p-ser727-STAT3 status may serve as a prognostic factor for breast cancer.
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 | 0.001 | 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) | 0.002 | 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".