Unique features of PTCH1 mutation spectrum in Chinese sporadic basal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Alterations of the PTCH1 gene have been found to contribute to both familial and sporadic basal cell carcinoma (BCC), especially in Caucasian patients. Furthermore, the majority of PTCH1 gene mutations in sporadic BCCs in Caucasian patients carry ultraviolet (UV) signatures, suggesting the key role of UV light in BCC development. However, sporadic BCC in non-Caucasian population has a lower incidence, and the pathogenesis remains largely unknown. To date, there has been no mutation analysis on PTCH1 gene in Chinese patients with sporadic BCCs. OBJECTIVE: To investigate genetic alterations of the PTCH1 gene in Chinese sporadic BCCs. METHODS: Direct sequencing was used to screen for mutations in PTCH1 in 31 microdissected samples in Chinese sporadic BCCs. In addition, single nucleotide polymorphisms (SNPs) were studied for loss of heterozygosity (LOH). RESULTS: Nineteen PTCH1 mutations in 17 of the 31 BCCs (54.8%) were identified. SNP analysis revealed LOH of PTCH1 in 10 of 23 BCCs (43.5%). Interestingly, the majority of mutations identified (63.2%) were insertion/deletion, which was different from the results in Caucasian cases whose mutations are predominantly point mutations. Only two (10.5%) of the remaining seven mutations were UV-specific C → T transition or tandem CC → TT transitions. All mutations occurred evenly throughout the entire PTCH1 protein domain without a hot-spot detected. CONCLUSION: Mutations and LOH in PTCH1 were also highly prevalent in Chinese sporadic BCCs. However, UV light plays a less role in causing these mutations, suggesting other potential mechanisms in the development of sporadic BCC in Chinese patients.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".