EpCAM nuclear localization identifies aggressive Thyroid Cancer and is a marker for poor prognosis
Bibliographic record
Abstract
BACKGROUND: Proteolytic cleavage of the extracellular domain (EpEx) of Epithelial cell adhesion molecule (EpCAM) and nuclear signaling by its intracellular oncogenic domain Ep-ICD has recently been implicated in increased proliferation of cancer cells. The clinical significance of Ep-ICD in human tumors remains an enigma. METHODS: EpEx, Ep-ICD and beta-catenin immunohistochemistry using specific antibodies was conducted on 58 archived thyroid cancer (TC) tissue blocks from 34 patients and correlated with survival analysis of these patients for up to 17 years. RESULTS: The anaplastic (ATC) and aggressive thyroid cancers showed loss of EpEx and increased nuclear and cytoplasmic accumulation of Ep-ICD. In contrast, the low grade papillary thyroid cancers (PTC) showed membranous EpEx and no detectable nuclear Ep-ICD. The ATC also showed concomitant nuclear expression of Ep-ICD and beta-catenin. Kaplan-Meier Survival analysis revealed reduced overall survival (OS) for TC patients showing nuclear Ep-ICD expression or loss of membranous EpEx (p < 0.0004), median OS = 5 months as compared to 198 months for patients who did not show nuclear Ep-ICD or demonstrated only membranous EpE. CONCLUSION: We report reciprocal loss of membrane EpEx but increased nuclear and cytoplasmic accumulation of Ep-ICD in aggressive TC; nuclear Ep-ICD correlated with poor OS of TC patients. Thus nuclear Ep-ICD localization may serve as a useful biomarker for aggressive TC and may represent a novel diagnostic, prognostic and therapeutic target for aggressive TC.
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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) | 0.001 | 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".