Heterogeneous ribonucleoprotein K is a marker of oral leukoplakia and correlates with poor prognosis of squamous cell carcinoma
Bibliographic record
Abstract
Oral leukoplakia is a heterogeneous lesion with risk of cancer development; there are no biomarkers to predict its potential of malignant transformation. Tissue proteomic analysis of oral leukoplakia using iTRAQ labeling liquid chromatography-mass spectrometry showed overexpression of heterogeneous ribonucleoprotein K (hnRNP K), a transformation-related RNA-binding protein, in leukoplakia in comparison with normal tissue. Herein, we investigated the clinical significance of hnRNP K in identification of oral leukoplakic lesions in early stages and as a prognostic marker in head-and-neck/oral squamous cell carcinomas (HNOSCCs). Immunohistochemical analysis of hnRNP K was performed in 100 HNOSCCs, 199 leukoplakias and 55 nonmalignant tissues and correlated with clinicopathologic parameters and disease prognosis over 6 years for HNOSCCs. hnRNP K nuclear expression increased from normal tissues to leukoplakia, and frank malignancy (p < 0.001). Cytoplasmic hnRNP K increased significantly from leukoplakia to HNOSCCs (p < 0.001) and was associated with poor prognosis of HNOSCCs (p = 0.011) by Kaplan-Meier analysis. The most important finding of our follow-up study is that cytoplasmic hnRNP K is an independent predictor of disease recurrence in HNOSCC patients. In conclusion, nuclear hnRNP K may serve as a potential marker for early diagnosis, whereas its cytoplasmic accumulation can help to identify a subgroup of HNOSCC patients with poor prognosis, suggesting its putative utility in clinical management of HNOSCC.
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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".