Effect of the TERT mutation on the prognosis of patients with urothelial carcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Telomerase reverse transcriptase (TERT) mutation represents the most prevalent genetic mutation found in urothelial carcinoma (UC) and holds potential as a prognostic indicator for tumor outcomes. However, the association between TERT mutation and prognosis in UC patients remains poorly elucidated due to conflicting findings in existing literature. Therefore, this study aimed to investigate the effect of the TERT mutation on the survival of UC patients. METHODS: We systematically searched the PubMed, Embase, and Cochrane Library databases for studies that investigated the relationship between the TERT mutation and the prognosis of UC patients. Endpoints included the 2-year and 5-year recurrence-free survival (RFS) and overall survival (OS). The Newcastle-Ottawa Scale (NOS) tool was used to assess the risk of bias in the included studies. Review Manager 5.3 was used for the meta-analysis. RESULTS: Nine studies with a total of 1,552 patients were included in the analysis. Two studies were prospective, and seven were retrospective. The TERT promoter mutation was associated with a lower 2-year OS (relative risk [RR] = 0.92, 95% confidence interval [CI] 0.86-0.98; P = 0.007) and a lower 5-year OS (RR = 0.80, 95% CI 0.68-0.94; P = 0.008) compared with the TERT wild type. However, no significantly differences were found between two groups in terms of HR for OS (hazard ratio [HR] = 1.29, 95% CI 0.80-2.08; P = 0.29). Furthermore, we investigated the differences in RFS and disease-specific survival (DSS) between the two groups. CONCLUSION: The TERT mutation increases the risk of death and decreases the survival time of UC patients. TERT may be a valuable marker with individual prognostic value.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".