Shorter telomere length as a prognostic marker for survival and recurrence in breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Telomere length is a potential prognostic biomarker in breast cancer, but its clinical utility remains uncertain due to inconsistent findings across the literature. This systematic review and meta-analysis aims to evaluate the association between telomere length and breast cancer survival outcomes, including overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and recurrence-free survival (RFS). Methods: A systematic search of ten sources, including databases and publishers (JSTOR, Nature, ProQuest, PubMed, Sage Journals, ScienceDirect, Science, Scopus, Springer, and Wiley) was conducted to identify studies published up to December 31, 2023. Studies reporting associations between telomere length and survival outcomes in breast cancer patients were included. Hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CI) were extracted or calculated. Quality assessment was performed using the Newcastle-Ottawa Scale, and publication bias was evaluated using funnel plots, Egger’s, and Begg’s tests. Results: Nine studies involving 3,145 breast cancer patients were included. Shorter telomere length was significantly associated with increased recurrence risk (DFS/RFS) (pooled HR: 1.97; 95% CI: 1.04–3.74, P = 0.039), indicating a nearly twofold increase in risk. Trends toward worse OS (pooled HR: 1.60; 95% CI: 0.90–2.86, P = 0.110) and DSS (pooled HR: 1.09; 95% CI: 0.80–1.49, P = 0.565) were observed, but did not reach statistical significance. Additionally, shorter telomere length was significantly associated with premenopausal status (pooled OR: 1.34; 95% CI: 1.06–1.70, P = 0.01). Discussion: Shorter telomere length is associated with an increased risk of recurrence in breast cancer, highlighting its potential as a prognostic biomarker. However, further research is needed to standardize telomere length measurement methodologies and validate these findings across diverse populations and breast cancer subtypes.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.007 | 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) | 0.003 | 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".