The -124C>T Mutation of the TERT Promoter Indicates Favorable Prognosis in Ovarian Clear Cell Carcinoma: A Single Institutional Study in China
Bibliographic record
Abstract
Background: Ovarian clear cell carcinoma (OCCC) is characterized by chemoresistance and poor prognosis in advanced or recurrent cases. This study aimed to find specific prognostic markers for OCCC. Methods: We analyzed 169 OCCC patients for clinicopathological features. TERT promoter and PIK3CA mutations were assessed by Sanger sequencing, and immunohistochemistry for ARID1A, HDAC6, Cyclin E1, and p53 was performed on tissue microarrays. Survival analysis was conducted using Kaplan–Meier and Cox regression models. Results: The -124C>T TERT promoter mutation was associated with longer OS and PFS and was an independent predictor of favorable OS. This mutation correlated with lower CA125 levels and higher SNP frequency. p53 mutations indicated advanced disease, bilateral tumors, reduced Cyclin E1, and poor prognosis. Low HDAC6 expression was linked to worse PFS. Mutual exclusivity was observed between PIK3CA exon 20 mutations and SNPs. Conclusions: The -124C>T TERT promoter mutation may serve as a favorable prognostic marker in OCCC, while p53 mutations and reduced HDAC6 expression are associated with poor outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".