Molecular Prognosticators and Genomic Instability in Papillary Thyroid Cancer
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Tumor progression has been attributed to the accumulation of DNA damage concurrent with selection of advantageous mutations; this DNA damage may result from failure to maintain genomic integrity or from susceptibility to carcinogens. Glutathione S-transferases (GSTs), enzymes that metabolize many carcinogens, may play a role in preserving genome integrity. The objectives of this study are to assess the relationship of GST genotypes with prognosis, clinicopathologic parameters, and genomic instability in papillary thyroid cancer. STUDY DESIGN: Prospective analysis. METHODS: GSTM1 and GSTT1 genotypes of 35 matched normal and papillary thyroid cancer specimens were determined by polymerase chain reaction (PCR) using primers specific for the coding sequences of each gene. Genomic instability was measured by intersimple sequence repeat PCR for each tumor/normal pair and compared with the GAMES prognostic scoring system and clinicopathologic parameters including age, extrathyroidal extension, tumor grade, size, stage metastasis, sex, and smoking history. RESULTS: GSTM1 and GSTT1 null genotypes were found in the normal tissues of 46% and 45%, respectively. No gene losses were detected in the tumor specimens. A significant association between the GSTM1 null genotype and increased risk of recurrence and death was observed. Elevated GII correlated with smoking and tumor stage but not with GST genotype. CONCLUSION: The association of GSTM1 null genotype with intermediate and high risk GAMES categories suggests that GSTM1 provides some protection against disease progression. However, this protection does not confer resistance to disease onset. GST genotyping may be a useful adjunct prognosticator with GAMES.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".