Physical interaction of STAT1 isoforms with TGF-β receptors leads to functional crosstalk between two signaling pathways in epithelial ovarian cancer
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Bibliographic record
Abstract
BACKGROUND: The signal transducer and activator of transcription (STAT) and transforming growth factor-β (TGF-β) signaling pathways play important roles in epithelial ovarian cancer (EOC). However, the mechanism of crosstalk between two pathways is not completely understood. METHODS: The expression of STAT1 protein was detected by tissue microarray and immunoblotting (IB). The interaction of STAT1 isoforms with TGF-β receptors was confirmed by immunoprecipitation and IB. The effect of TGF-β signaling on STAT1 activation was examined in EOC and non-tumorous HOSEpiC cells treated with TGF-β1 in the presence or absence of the inhibitor of TGF-β type I receptor. The gain-of-function and loss-of-function approaches were applied for detecting the role of STAT1 on EOC cell behaviours. RESULTS: The high level of STAT1 was observed in patients with high-grade serous EOC. STAT1 expression was higher in ovarian cancer cells than noncancerous cells. TGF-β1 activated the STAT1 pathway by inducing the phosphorylation of STAT1α on S727 residue. The full-length STAT1α and the truncated STAT1β directly interacted with TGF-β receptors (ALK1/ALK5 and TβRII), which was mediated by TGF-β1. STAT1α and STAT1β blocked the activation of the TGF-β1 signaling pathway in EOC cells by reducing Smad2 phosphorylation. STAT1 overexpression induced EOC cell proliferation, migration, and invasion; whereas its inhibition enhanced TGF-β1-induced phospho-Smad2 and suppressed EOC cell proliferation, migration, and invasion. CONCLUSIONS: Our data unveil a novel insight into the molecular mechanism of crosstalk between the STAT1 and TGF-β signaling pathways, which affected the cancer cell behavior. Suppression of STAT1 may be a potential therapeutic strategy for targeting ovarian cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it