DJ-1 Could Predict Worse Prognosis in Esophageal Squamous Cell Carcinoma
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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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) | 0.002 | 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".