Subcellular differential expression of Ep-ICD in oral dysplasia and cancer is associated with disease progression and prognosis
Bibliographic record
Abstract
BACKGROUND: Identification of patients with oral dysplasia at high risk of cancer development and oral squamous cell carcinoma (OSCC) at increased risk of disease recurrence will enable rigorous personalized treatment. Regulated intramembranous proteolysis of Epithelial cell adhesion molecule (EpCAM) resulting in release of its intracellular domain Ep-ICD into cytoplasm and nucleus triggers oncogenic signaling. We analyzed the expression of Ep-ICD in oral dysplasia and cancer and determined its clinical significance in disease progression and prognosis. METHODS: In a retrospective study, immunohistochemical analysis of nuclear and cytoplasmic Ep-ICD and EpEx (extracellular domain of EpCAM), was carried out in 115 OSCC, 97 oral dysplasia and 105 normal oral tissues, correlated with clinicopathological parameters and disease outcome over 60 months for oral dysplasia and OSCC patients. Disease-free survival (DFS) was determined by Kaplan-Meier method and multivariate Cox regression analysis. RESULTS: In comparison with normal oral tissues, significant increase in nuclear Ep-ICD and membrane EpEx was observed in dysplasia, and OSCC (p = 0.013 and < 0.001 respectively). Oral dysplasia patients with increased overall Ep-ICD developed cancer in short time period (mean = 47 months; p = 0.044). OSCC patients with increased nuclear Ep-ICD and membrane EpEx had significantly reduced mean DFS of 33.7 months (p = 0.018). CONCLUSIONS: Our study provided clinical evidence for Ep-ICD as a predictor of cancer development in patients with oral dysplasia and recurrence in OSCC patients, suggesting its potential utility in enhanced management of those patients detected to have increased risk of progression to cancer and recurrence in OSCC patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".