The clinical and biological significance of STAT1 in esophageal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Loss of STAT1 (Signal Transducer and Activator of Transcription-1) has been implicated in the pathobiology of a number of cancer types. Nonetheless, the biological and clinical significance of STAT1 in esophageal squamous cell carcinomas (ESCC) has not been comprehensively studied. METHODS: Using immunohistochemistry, we detected the STAT1 expression in a cohort of ESCC patients; In-vitro experiments, we used enforced gene transfection of STAT1C into two STAT1- weak/negative ESCC cell lines and siRNA knockdown of STAT1 in two STAT1-strong ESCC cell lines to detect STAT1 function in ESCC. RESULTS: We found that the expression of STAT1 was heterogeneous in ESCC, with 64 (49.0%) strongly positive cases, 59 (45.0%) weakly positive cases and 8 (6.1%) negative cases. STAT1 expression inversely correlated with the depth of tumor invasion and tumor size (p=0.047 and p=0.029, respectively, Chi square). Furthermore, patients with STAT1-strong/weak tumors had a significantly longer survival compared to those with STAT1-negative tumors (33.6 months versus 13.1 months, p=0.019). In patients carrying tumors of aggressive cytology (n=50), those with STAT1-strong tumors survived significantly longer than those with STAT1-weak/negative tumors (34.6 months versus 20.5 months, p=0.011). Our in-vitro experiments revealed that STAT1 is proapoptotic and inhibitory to cell-cycle progression and colony formation. Lastly, we found evidence that STAT1 signaling in ESCC cells down-regulated the expression and/or activity of NF-κB and STAT3, both of which are known to have oncogenic potential. CONCLUSION: To conclude, our findings suggest that STAT1 is a tumor suppressor in ESCC. Loss of STAT1, which is frequent in ESCC, contributes to the pathogenesis of these tumors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".