TP53 and c-myc Co-alterations: A hallmark of oral cancer progression
Bibliographic record
Abstract
Background: Head and neck squamous cell carcinoma, including oral cancer, is the sixth most common cancer worldwide. Despite advances in surgery and treatment, the 5-year survival rate has not improved significantly. Therefore, reliable molecular markers for oral cancer progression are badly needed. Methods: We conducted a copy number analysis to estimate amplification status of c-myc, cycD1 and EGFR oncogenes, mutational PCR-SSCP analysis to determine activation of H-ras oncogene and inactivation of TP53 tumour suppressor gene and methylation specific PCR analysis to evaluate hypermethylation of p16 and MGMT genes. Results: c-myc oncogene was amplified in 56.7%, cycDI in 20% and EGFR in 16.7% of Oral Squamous Cell Carcinoma (OSCC) cases while H-ras was activated in 33.3% of samples. Amplification of c-myc was significantly associated with the tumour grade 2. Interestingly, EGFR and H-ras alterations were mutually exclusive. p16 and MGMT were inactivated by hypermethylation in 30% and 13.3% of cases. Co-alteration of cycDI and p16 were not observed in any of the analyzed samples. TP53 was inactivated in 56.7% of samples and was significantly associated with progression of OSCC, grade 2 and stage 2. Moreover, TP53 and c-myc oncogene were simultaneously altered in grade 2 OSCC. Conclusions: The most promising marker of OSCC progression remains the TP53 tumour suppressor, which is the most frequently mutated gene in oral cancers. Since there is synergism between TP53 and c-myc, it seems that co-alteration of these two genes could be also a good marker of OSCC progression from grade1 to grade 2 tumours.
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 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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".