The steroid receptor RNA activator protein is expressed in breast tumor tissues
Bibliographic record
Abstract
The steroid receptor RNA activator (SRA) was originally described as the first functional noncoding RNA able to specifically coactivate the activity of steroid receptors. We previously demonstrated the existence in breast cancer cell lines of new SRA isoforms that, as opposed to the first cloned SRA RNA, encode for a 236-amino acid protein, SRAP. To investigate the possible implications of the coding SRA RNA and SRAP expression on breast cancer progression, we examined by Western blot analysis 74 primary breast tumors of patients subsequently treated with tamoxifen. Patients whose primary tumors were positive for SRAP expression (n = 24) had a significantly (Kaplan-Meier survival curve p = 0.047) lower likelihood of dying from recurrent disease than SRAP-negative patients (n = 50). We generated 2 cell lines, SRAP-V5-High.A and SRAP-V5-High.B, by stably overexpressing SRAP in the estrogen receptor-positive MCF-7 breast cancer cell line. Transient transfection experiments, performed using a luciferase reporter gene under the control of an estrogen-responsive element, revealed decreased sensitivity to estradiol but no additional sensitivity to tamoxifen in SRAP-overexpressing cells. Overall, our data suggest that the presence of both coding SRA RNA and its corresponding SRAP modifies the activity of estrogen receptor in breast cancer cells and that SRAP could be a new clinical marker for breast cancer. Further studies are needed to define the respective mechanisms of action and the roles of SRA RNA and protein in breast tumorigenesis and tumor progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".