The role of constitutively active signal transducer and activator of transcription 3 in ovarian tumorigenesis and prognosis
Bibliographic record
Abstract
BACKGROUND: Signal transducer and activator of transcription 3 (Stat3), which is a latent transcription factor that participates in the transcriptional activation of apoptosis and cell cycle progression, has been implicated as an oncogene in several neoplastic diseases. However, the specific role of Stat3 in ovarian carcinogenesis remains poorly understood. The objectives of the current study were to examine the effect of Stat3 activation on the phenotypic transformation of an immortalized, nontumorigenic ovarian epithelial cell line and to evaluate the expression of tyrosine-activated Stat3 (pStat3) in tissue microarrays from 303 ovarian carcinomas to determine its prognostic relevance and to correlate its expression with several upstream oncogenes of Stat3 and with the oncogenes involved in apoptosis and proliferation. METHODS: Overexpression of pStat3 was weakly tumorigenic and produced measurable tumors in mice in 1 of 3 clones. Using tissue microarrays from a large group of patients with primary ovarian carcinoma, the expression of pStat3 was correlated with the expression of growth factor receptors (HER-2/neu and epidermal growth factor receptor [EGFR]), interleukin 6, and the proliferation and apoptosis markers Ki-67, Bcl-2, and Bcl-xL and with clinicopathologic variables and patient survival. RESULTS: High pStat3 expression in the tumor tissue microarray was associated with high levels of HER-2/neu, EGFR, and Ki-67. No correlation was observed between overall pStat3 levels and any other clinicopathologic variables tested. High nuclear expression of pStat3 (>10% of positive-stained cells) was linked with poor overall survival. CONCLUSIONS: The activation and translocation of pStat3 to the nucleus are frequent events in ovarian carcinoma that are associated with a poor prognosis. Further studies are needed to elucidate the mechanism of activation of Stat3, its effects on downstream targets, and its role in the neoplastic transformation of epithelial ovarian cells.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".