Genome-wide profiling of transfer RNAs and their role as novel prognostic markers for breast cancer
Bibliographic record
Abstract
Transfer RNAs (tRNAs, key molecules in protein synthesis) have not been investigated as potential prognostic markers in breast cancer (BC), despite early findings of their dysregulation and diagnostic potential. We aim to comprehensively profile tRNAs from breast tissues and to evaluate their role as prognostic markers (Overall Survival, OS and Recurrence Free Survival, RFS). tRNAs were profiled from 11 normal breast and 104 breast tumor tissues using next generation sequencing. We adopted a Case-control (CC) and Case-Only (CO) association study designs. Risk scores constructed from tRNAs were subjected to univariate and multivariate Cox-proportional hazards regression to investigate their prognostic value. Of the 571 tRNAs profiled, 76 were differentially expressed (DE) and three were significant for OS in the CC approach. We identified an additional 11 tRNAs associated with OS and 14 tRNAs as significant for RFS in the CO approach, indicating that CC alone may not capture all discriminatory tRNAs in prognoses. In both the approaches, the risk scores were significant in the multivariate analysis as independent prognostic factors, and patients belonging to high-risk group were associated with poor prognosis. Our results confirmed global up-regulation of tRNAs in BC and identified tRNAs as potential novel prognostic markers for BC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".