Young Patients With Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate whether oral squamous cell carcinomas (OSCCs) from young (</=40 years) and older (>/=60 years) patients have differential expression levels of GSTP1, FANCA, FANCC, FANCD2, and FANCG. DESIGN: Quantitative real-time reverse transcriptase-polymerase chain reaction and immunohistochemical analysis were used to assess gene and protein expression, respectively. SETTING: This study was performed in a research institute within a hospital setting. PATIENTS: Our study group consisted of 104 patients (42 young and 62 older). We collected RNA from 32 OSCC samples (10 young and 22 older patients) for gene expression analysis. Seventy-seven OSCC samples (37 from young and 40 from older patients) were used for protein expression analysis. Five patients were studied in both analyses. RESULTS: Lower expression of GSTP1 (P = .04) and FANCA (P = .01) was observed in the tumors of young compared with older patients. We also detected lower expression of GSTP1 in the tumors of young patients compared with their nondysplastic mucosa (P = .01). FANCA was underexpressed in nondysplastic mucosa of young compared with older patients (P = .01). GSTP1 protein showed negative or low expression in 41% (n = 15 of 37) of young vs 5% (n = 2 of 40) of older patient tumors (P = .001). FANCG protein expression was absent or low in 81% (n = 30 of 37) of young compared with 36% (n = 15 of 40) of older patient tumors (P<.001). CONCLUSIONS: Differences in expression levels of GSTP1, FANCA, and FANCG in OSCC of young and older patients suggest that different mechanisms may be involved in tumor development through defective carcinogen metabolism and/or DNA repair capabilities. GSTP1 plays a key role in detoxification; therefore, underexpression of this gene in tumors of young patients may cause deficient detoxification that could lead to an increased susceptibility to the development of oral carcinoma.
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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 | 0.001 |
| 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) | 0.003 | 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".