The Fer Tyrosine Kinase Cooperates with Interleukin-6 to Activate Signal Transducer and Activator of Transcription 3 and Promote Human Prostate Cancer Cell Growth
Why this work is in the frame
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Bibliographic record
Abstract
Androgen withdrawal is the most effective form of systemic therapy for men with advanced prostate cancer. Unfortunately, androgen-independent progression is inevitable, and the development of hormone-refractory disease and death occurs within 2 to 3 years in most men. The understanding of molecular mechanisms promoting the growth of androgen-independent prostate cancer cells is essential for the rational design of agents to treat advanced disease. We previously reported that Fer tyrosine kinase level correlates with the development of prostate cancer and aggressiveness of prostate cancer cell lines. Moreover, knocking down Fer expression interferes with prostate cancer cell growth in vitro. However, the mechanism by which Fer mediates prostate cancer progression remains elusive. We present here that Fer and phospho-Y705 signal transducer and activator of transcription 3 (STAT3) are barely detectable in human benign prostate tissues but constitutively expressed in the cytoplasm and nucleus of the same subsets of tumor cells in human prostate cancer. The interaction between STAT3 and Fer was observed in all prostate cancer cell lines tested, and this interaction is mediated via the Fer Src homology 2 domain and modulated by interleukin-6 (IL-6). Moreover, IL-6 triggered a rapid formation of Fer/gp130 and Fer/STAT3 complexes in a time-dependent manner and consistent with changes in Fer and STAT3 phosphorylation and cytoplasmic/nuclear distribution. The modulation of Fer expression/activation resulted in inhibitory or stimulatory effects on STAT3 phosphorylation, nuclear translocation, and transcriptional activation. These effects translated in IL-6-mediated PC-3 cell growth. Taken together, these results support an important function of Fer in prostate 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 | 0.000 | 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) | 0.000 | 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