DJ-1 Could Predict Worse Prognosis in Esophageal Squamous Cell Carcinoma
Why this work is in the frame
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Bibliographic record
Abstract
Recent studies have revealed an oncogenic role of DJ-1 through its ability to transform normal cells, prevent oxidative damage, and inhibit apoptosis. However, its role in esophageal squamous cell carcinoma (ESCC) is unknown. In this study, by immunohistochemistry, we analyzed the expression of DJ-1 in 81 ESCC tumors, 31 paired nonneoplastic esophageal epithelia, and 19 paired ESCC lymph node metastases. We found that cytoplasmic DJ-1 expression was significantly higher in ESCC and ESCC lymph node metastases than in nonneoplastic esophageal epithelium. ESCC specimens with high distant metastatic potential also had a significantly higher level of nuclear DJ-1 expression (P = 0.018). By Kaplan-Meier analysis, we found that a high level of nuclear DJ-1 was significantly associated with poorer patient survival in our cohort (P = 0.028). To investigate whether DJ-1 promotes ESCC progression through phosphatidylinositol 3-kinase pathway and modulation of apoptosis, we performed immunohistochemistry of pAkt and Daxx. We found that DJ-1 expression was significantly associated with pAkt, whereas nuclear DJ-1 expression was significantly correlated with nuclear expression of Daxx. These results suggest that phosphatidylinositol 3-kinase pathway and Daxx-regulated apoptosis might be important in DJ-1-mediated ESCC progression. By using multivariate Cox regression, we further showed that T(4) stage (P = 0.003) and DJ-1 (P = 0.034) are independent predictors of patient survival. In conclusion, our results suggest that DJ-1 plays a very important role in transformation and progression of ESCC and may be used as a prognostic marker in ESCC.
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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 | 0.001 | 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 | 0.001 | 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