Prognostic Role of <i>KRAS</i> mRNA Expression in Breast Cancer
Bibliographic record
Abstract
Purpose: We investigated the prognostic role of KRAS mRNA expression in breast cancer using The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases.Methods: Clinical and biological data of 1,093 breast cancers from TCGA database and 1,904 breast cancers from METABRIC database were analyzed.Overall survival (OS) and breast cancer-specific survival (BCSS) were determined.Results: The group with high KRAS mRNA expression showed worse survival than the group with low KRAS mRNA expression regarding both OS (p = 0.012 in TCGA, p < 0.001 in METABRIC) and BCSS (p = 0.001 in METABRIC).According to multivariate analysis, the level of KRAS mRNA expression was an independent prognostic factor in both TCGA (hazard ratio [HR], 1.570; 95% confidence interval [CI], 1.026-2.403;p = 0.038) and METABRIC (HR, 1.254; 95% CI, 1.087-1.446;p = 0.002) databases.The prognostic impact of mRNA expression was effective only for luminal A subtype (p < 0.001 in METABRIC).Positive correlation was observed between mRNA expression and copy number alteration (CNA) (r = 0.577, p < 0.001 in TCGA; ρ = 0.343, p < 0.001 in METABRIC).Methylation showed negative correlations with both mRNA expression and CNA (r = -0.272,p < 0.001 in TCGA).The expression of mRNA had little association with the mutation status in breast cancers, having a mutation frequency of approximately 0.6%.Conclusion: KRAS mRNA expression was significantly associated with breast cancer prognosis.It was found to be an independent prognostic factor for breast cancer.Prognostic role of KRAS mRNA expression was effective only in luminal A subtype.Further studies are needed to validate the prognostic role of KRAS mRNA expression in breast cancer, thus paving a way for clinical application of KRAS in practice.
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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 | 0.001 | 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".