Prolactin hormone exerts anti-tumorigenic effects in HER-2 overexpressing breast cancer cells through regulation of stemness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Breast cancers characterized by HER2 overexpression, belong to HER-2 enriched or luminal B subtypes, are frequently associated with higher incidence of tumor recurrence and therapeutic failure. These aggressive features have been attributed to the presence of cancer stem-like cell subpopulations known to have high tumor initiation, self -renewal capacities and high metastatic potential. Depleting these stem-like cells in these tumors therefore might help in improving therapeutic response and patient outcome. METHODS: Here we used human breast cancer cells representative of HER2- enriched and luminal B subtypes as well as purified ALDH-positive stem-like cell subpopulation for in vitro cell viability, proliferation, tumorshpere formation analyses and gene expression studies. In addition, we used a pre-clinical xenograft HER2 mouse model (NOD/SCID mice) for in vivo tumorigenesis assessment. Furthermore, patient survival outcomes were evaluated using in silico bioinformatics analyses of publicly available datasets. RESULTS: stem-like subpopulation. Furthermore, we show PRL to impede tumor growth of HER-2 xenografts and to suppress expression of Ki67 proliferative marker. Finally, we found PRL pathway gene signature to correlate with favorable patient outcomes in HER-2 and luminal B breast cancer patients. CONCLUSION: Together these results emphasize an anti-tumorigenic role with a potential therapeutic value for PRL in HER-2 and luminal B breast cancer subtypes targeting the cancer stem-like cells.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it