267 INTESTINAL STEM CELL MARKERS AND ITS POTENTIAL USE IN THE CLINICOPATHOLOGICAL SETTING OF ESOPHAGEAL ADENOCARCINOMA
Bibliographic record
Abstract
Abstract Barrett’s esophagus (BE) is the primary precursor lesion of esophageal adenocarcinoma (EAC), which not only resembles the intestinal mucosa morphologically but also expresses various intestinal stem cell (ISC) markers. We hypothesized that ISC markers, Lgr5 (also a cancer stem cell marker), Ascl2 (fate determinator of ISC),Bmi1 (quiescent counterpart of Lgr5) and Cdx2 (primary regulator of ISC gene expression) have clinicopathological significance and could potentially be a predictor for survival in EAC. Methods Tissue microarray consisted of 64 EAC and 22 BE, and the expressions of Lgr5, Ascl2, Bmi1 and Cdx2 were analyzed using immunohistochemistry and scored independently by two pathologists. Clinicopathological factors (age, pathological grade and stage, affected lymph nodes, neoadjuvant therapy) were confounding factors, and univariable analysis using Fisher's exact tests as well as survival analysis using the Kaplan–Meier (KM) method and Cox proportional hazards regression (Cox PH) were performed to investigate its statistical significance. We performed a bioinformatic analysis of the TCGA dataset to validate the immunohistochemical findings. Results Among EAC, 69%, 88%, 64% and 70% expressed high Ascl2, Lgr5, Bmi1 and Cdx2, respectively. High Ascl2 and low Lgr5 expression significantly correlated to a higher number of involved lymph nodes; high Bmi1 expression significantly correlated to the pathological stage. Cdx2 was not correlated to any markers. KM analysis showed a negative impact of high Ascl2 expression on overall survival (OS; p = 0.0276) as well as progression-free survival (PFS; p = 0.0466), but not Lgr5, Bmi1 nor Cdx2. Cox PH analysis revealed Ascl2 (p = 0.011), and Cdx2 (p = 0.015) expression are independent prognostic factors for EAC. Conclusion Our results suggest that among the four ISC markers, Ascl2 and Cdx2 protein holds potential to be utilized as a prognostic biomarker. TCGA dataset revealed the association of ASCL2 mRNA expression with the number of positive lymph nodes but not overall survival, which implies further research is needed to explain the mechanism of Ascl2 overexpression in EAC carcinogenesis via ISC regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".