Steroid Receptor RNA Activator Protein (SRAP): A Potential New Prognostic Marker for Estrogen Receptor-Positive/Node-Negative/Younger Breast Cancer Patients.
Bibliographic record
Abstract
Abstract Purpose: The steroid receptor RNA activator (SRA) is a functional RNA suspected to participate in the mechanisms underlying breast tumor progression. This RNA is also able to encode for a protein, SRAP, whose exact function remains to be determined. Our aim was to assess, in a large breast cancer cohort, whether levels of this protein could be associated with outcome or established clinical parameters.Experimental Design: Following antibody validation, we have assessed SRAP expression by tissue-microarray (TMA) analysis of 372 tumors with known steroid receptor and node status. Clinical follow-up was available for all the corresponding patients. Immunohistochemical scores were independently determined by two investigators and averaged. Statistical analyses were performed using standard univariate and multivariate tests.Results: SRAP levels were significantly (Mann-Whitney rank sum test, P<0.05) higher in estrogen receptor-alpha positive (ER+, n = 273), in progesterone receptor positive (PR+, n= 256) and in older patients (age ≥ 65 years, n = 183). When considering ER+ tumors, PR+ tumors, or young patients (< 65 years), patients with high SRAP expression had a significantly (Mantel-Cox test, P < 0.05) worse breast cancer specific survival (BCSS) than patients with low SRAP levels. SRAP also appeared as a very powerful indicator of poor prognostic for BCSS in the subset of ER+, node negative and young breast cancer patients (Cox regression analysis, n = 60, BCSS Hazard Ratio=13.937, P<0.0001).Conclusion: Our data suggest that SRAP might be a new predictor of breast cancer specific survival in younger breast cancer patients with ER+/node negative tumors. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2017.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".