Intestinal Stem Cell Marker ASCL2 is a Novel Prognostic Predictor in Esophageal Adenocarcinoma
Bibliographic record
Abstract
Purpose Intestinal stem cell markers play a significant role in esophageal adenocarcinoma carcinogenesis via Barrett's esophagus; however, its utility as a prognostic biomarker has not been established. Methods We analyzed the immunohistochemical expression of intestinal stem cell markers, ASCL2 and LGR5, using whole slides (35 cases) and tissue microarray (TMA; 64 cases). On TMA slides, adjacent normal squamous epithelium, metaplastic glandular epithelium (Barrett's esophagus), and dysplastic glandular epithelium were inserted when applicable. Two pathologists semi-quantitatively scored stained slides independently, and the results were correlated with clinicopathologic factors and outcomes. Results In whole slides, 51% and 57% expressed high ASCL2 and high LGR5; in TMA, 69% and 88% expressed high ASCL2 and high LGR5, respectively. In TMA, high ASCL2 and low LGR5 expression significantly correlated to a higher number of involved lymph nodes (p=0.027 and p=0.0039), and LGR5 expression significantly correlated to the pathological stage (p=0.0032). Kaplan-Meier analysis showed a negative impact of high ASCL2 expression on overall survival (OS; WS p=0.0168, TMA p=0.0276) as well as progression-free survival (PFS; WS p=0.000638, TMA p=0.0466) but not LGR5. Multivariate Cox regression analysis revealed that ASCL2 expression is an independent prognostic factor for esophageal adenocarcinoma (OS; WS p=0.25, TMA p=0.011. PFS; WS p=0.012, TMA p=0.038). Analysis of the TCGA dataset showed that ASCL2 mRNA levels were correlated to nodal status but not overall survival. Conclusion High expression of the intestinal stem cell marker ASCL2 may predict unfavorable outcomes in surgically resected esophageal adenocarcinoma.
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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 | 0.001 |
| 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".