Recurrent genomic alterations in sequential progressive leukoplakia and oral cancer: drivers of oral tumorigenesis?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A significant proportion (up to 62%) of oral squamous cell carcinomas (OSCCs) may arise from oral potential malignant lesions (OPMLs), such as leukoplakia. Patient outcomes may thus be improved through detection of lesions at a risk for malignant transformation, by identifying and categorizing genetic changes in sequential, progressive OPMLs. We conducted array comparative genomic hybridization analysis of 25 sequential, progressive OPMLs and same-site OSCCs from five patients. Recurrent DNA copy number gains were identified on 1p in 20/25 cases (80%) with minimal, high-level amplification regions on 1p35 and 1p36. Other regions of gains were frequently observed: 11q13.4 (68%), 9q34.13 (64%), 21q22.3 (60%), 6p21 and 6q25 (56%) and 10q24, 19q13.2, 22q12, 5q31.2, 7p13, 10q24 and 14q22 (48%). DNA losses were observed in >20% of samples and mainly detected on 5q31.2 (35%), 16p13.2 (30%), 9q33.1 and 9q33.29 (25%) and 17q11.2, 3p26.2, 18q21.1, 4q34.1 and 8p23.2 (20%). Such copy number alterations (CNAs) were mapped in all grades of dysplasia that progressed, and their corresponding OSCCs, in 70% of patients, indicating that these CNAs may be associated with disease progression. Amplified genes mapping within recurrent CNAs (KHDRBS1, PARP1, RAB1A, HBEGF, PAIP2, BTBD7) were selected for validation, by quantitative real-time PCR, in an independent set of 32 progressive leukoplakia, 32 OSSCs and 21 non-progressive leukoplakia samples. Amplification of BTBD7, KHDRBS1, PARP1 and RAB1A was exclusively detected in progressive leukoplakia and corresponding OSCC. BTBD7, KHDRBS1, PARP1 and RAB1A may be associated with OSCC progression. Protein-protein interaction networks were created to identify possible pathways associated with OSCC progression.
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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 it