Reduction in Membranous Immunohistochemical Staining for the Intracellular Domain of Epithelial Cell Adhesion Molecule Correlates with Poor Patient Outcome in Primary Colorectal Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Epithelial cell adhesion molecule (epcam) is a multifunctional transmembrane glycoprotein expressed on both normal epithelium and epithelial neoplasms such as gastric, breast, and renal carcinomas. Recent studies have proposed that the proteolytic cleavage of the intracellular domain of epcam (epcam-icd) can trigger signalling cascades leading to aggressive tumour behavior. The expression profile of epcam-icd has not been elucidated for primary colorectal carcinoma. In the present study, we examined epcam-icd immunohistochemical staining in a large cohort of patients with primary colorectal adenocarcinoma and assessed its performance as a potential prognostic marker. METHODS: Immunohistochemical staining for epcam-icd was assessed on tissue microarrays consisting of 137 primary colorectal adenocarcinoma samples. Intensity of staining for each core was scored by 3 independent pathologists. The membranous epcam-icd staining score was calculated as a weighted average from 3 core samples per tumour. Univariate analysis of the average scores and clinical outcome measures was performed. RESULTS: The level of membranous epcam-icd staining was positively associated with well-differentiated tumours (p = 0.01); low preoperative carcinoembryonic antigen (p = 0.001); and several measures of survival, including 2-year (p = 0.02) and 5-year survival (p = 0.05), and length of time post-diagnosis (p = 0.03). A number of other variables-including stage, grade, and lymph node status-showed correlations with epcam staining and markers of poor outcome, but did not reach statistical significance. CONCLUSIONS: Low membranous epcam-icd staining might be a useful marker to identify tumours with aggressive clinical behavior and potential poor prognosis and might help to select candidates who could potentially benefit from treatment targeting epcam.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".