Significance and Mechanism of Microsatellite Instability in Laryngeal Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To evaluate the significance and mechanism of microsatellite instability (MSI) in laryngeal squamous cell carcinoma (LSCC). METHODS: We investigated the expression frequency and clinical significance of MSI in 50 LSCC patients. The status of MSI was evaluated by using microdissection, polymerase chain reaction, single-strand length polymorphism, and silver staining. Five markers on chromosomes 1p, 3p, 5q, 9p, and 17p were used. Two of the six components of mismatch repair (MMR)-hMLH1 and hMSH2-were investigated by an immunohistochemical approach. RESULTS: The informative case numbers of the five markers (D17S796, D3S3544, D5S656, D1S375, D9S162) were 44, 42, 45, 44, and 40 in all 50 cases, respectively. The incidence of MSI on D17S796 (TP53) was 20.5% (9 of 44), on D3S3544 (FHIT) was 14.3% (6 of 42), on D5S656 (APC) was 31.1% (14 of 45), on D1S375 (BCAR3) was 20.5% (9 of 44), and on D9S162 (CDKN2A) was 15.0% (6 of 40). Although there was no relationship between MSI status and age, gender, smoking history, tumour location, tumour differentiation, and T stage (p > .05), there was a strong relationship between MSI and relapse condition (p < .01). Also, MSI status correlated with MMR expression to some degree (p < .01). But it was common that negative and positive staining of MMR coexisted on the same slide. CONCLUSION: MSI and abnormal MMR may contribute to the carcinogenesis of a subset of LSCC. MSI may be a characteristic signal of tumour recurrence.
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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 | 0.001 | 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".