Concomitant Expression of Prolactin Receptor and TGFβ Receptors in Breast Cancer: Association with Less Aggressive Phenotype and Favorable Patient Outcome
Bibliographic record
Abstract
The epithelial-mesenchymal transition (EMT) process is known to play an essential role in tumor progression, metastasis and resistance to therapy. This report evaluated the prognostic value of co-expression of the receptor for prolactin (PRLR), a suppressor of EMT, and the receptors for transforming growth factor β (TGFβRI and TGFβRII), an inducer of EMT, in association with different clinicopathological parameters using TMA of 102 breast cancer patients and publicly available data on breast cancer patients. Interestingly, the results revealed that malignant tissues had significantly lower levels of concomitant protein expression of these receptors in comparison to normal/benign breast tissue. In addition, a higher level of concomitant expression was also observed in less aggressive breast cancer phenotypes, including low grade tumors, luminal breast cancer subtype, and less advanced stages of the disease (lymph node negative and early stages). Moreover, the results also showed that the expression of a gene signature composed of PRLR/TGFβRI/TGFβRII correlates more with differentiated grade I tumors, and identified a subset of patients showing better survival outcomes evident in luminal B and HER-2 enriched molecular subtypes. Together, these results indicate that loss of the co-expression of PRLR, TGFβRI and TGFβRII is indicative of aggressiveness and poor patient survival outcomes in 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".