Overexpression of Prothymosin Alpha Predicts Poor Disease Outcome in Head and Neck Cancer
Bibliographic record
Abstract
BACKGROUND: In our recent study, tissue proteomic analysis of oral pre-malignant lesions (OPLs) and normal oral mucosa led to the identification of a panel of biomarkers, including prothymosin alpha (PTMA), to distinguish OPLs from histologically normal oral tissues. This study aimed to determine the clinical significance of PTMA overexpression in oral squamous cell hyperplasia, dysplasia and head and neck squamous cell carcinoma (HNSCC). METHODOLOGY: Immunohistochemistry of PTMA protein was performed in HNSCCs (n = 100), squamous cell hyperplasia (n = 116), dysplasia (n = 50) and histologically normal oral tissues (n = 100). Statistical analysis was carried out to determine the association of PTMA overexpression with clinicopathological parameters and disease prognosis over 7 years for HNSCC patients. RESULTS: Our immunohistochemical analysis demonstrated significant overexpression of nuclear PTMA in squamous cell hyperplasia (63.8%), dysplasia (50%) and HNSCC (61%) in comparison with oral normal mucosa (p(trend)<0.001). Chi-square analysis showed significant association of nuclear PTMA with advanced tumor stages (III+IV). Kaplan Meier survival analysis indicated reduced disease free survival (DFS) in HNSCC patients (p<0.001; median survival 11 months). Notably, Cox-multivariate analysis revealed nuclear PTMA as an independent predictor of poor prognosis of HNSCC patients (p<0.001, Hazard's ratio, HR = 5.2, 95% CI = 2.3-11.8) in comparison with the histological grade, T-stage, nodal status and tumor stage. CONCLUSIONS: Nuclear PTMA may serve as prognostic marker in HNSCC to determine the subset of patients that are likely to show recurrence of the disease.
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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 | 0.000 |
| 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) | 0.001 | 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".