Prolactin Receptor Expression is an Independent Favorable Prognostic Marker in Human Breast Cancer
Bibliographic record
Abstract
Prolactin (PRL) hormone plays an important role in the development of the mammary gland and terminal differentiation of the mammary epithelial cells. While initial studies suggested that PRL may contribute to the development of breast cancer through PRL/prolactin receptor (PRLR) autocrine function, mounting evidence indicate a different role for PRL, highlighting this hormone as a regulator of epithelial plasticity and as a potential tumor suppressor. To gain further insights into the role of PRL in human breast carcinogenesis, immunohistochemistry analyses of PRLR protein expression levels using tissue microarray of 102 cases were done in comparison with various clinical/pathologic parameters and molecular subtypes. In addition, gene expression level of PRLR was also evaluated in relation to intrinsic molecular subtypes, tumor grade, and patient outcome using GOBO database for 1881 breast cancer patients. Interestingly, PRLR expression was found to be significantly downregulated in invasive breast cancer (21.4%) in comparison with normal/benign (80%) and in situ carcinoma (60%) (P=0.003498). Moreover, PRLR expression was associated with lymph node negativity and low-grade well-differentiated tumors. PRLR expression was strongest in luminal A subtype, and was virtually undetectable in the worse prognosis triple-negative breast cancer subtype (P=0.00001). Furthermore, PRLR expression was independent of ER, PR, HER-2, and P53 status. Finally, PRLR expression was significantly (P<0.01) associated with prolonged distant metastasis-free survival in breast cancer patients. In conclusion, our results highlight PRLR as an independent predictor of favorable prognosis in human breast cancer.
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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) | 0.001 | 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".