Bibliographic record
Abstract
The normal developmental program that prolactin generates in the mammary gland is usurped in the cancerous process and can be used out of its normal cellular context at a site of secondary metastasis. Prolactin is a pleiotropic peptide hormone and cytokine that is secreted from the pituitary gland, as well as from normal and cancerous breast cells. Experimental and epidemiologic data suggest that prolactin is associated with mammary gland development, and also the increased risk of breast tumors and metastatic disease in postmenopausal women. Breast cancer spreads to the bone in approximately 70% of cases with advanced breast cancer. Despite treatment, new bone metastases will still occur in 30%-50% of patients. Only 20% of patients with bone metastases survive five years after the diagnosis of bone metastasis. The breast cancer cells in the bone microenvironment release soluble factors that engage osteoclasts and/or osteoblasts and result in bone breakdown. The breakdown of the bone matrix, in turn, enhances the proliferation of the cancer cells, creating a vicious cycle. Recently, it was shown that prolactin accelerated the breast cancer cell-mediated osteoclast differentiation and bone breakdown by the regulation of breast cancer-secreted proteins. Interestingly, prolactin has the potential to affect multiple proteins that are involved in both breast development and likely bone metastasis, as well. Prolactin has normal bone homeostatic roles and, combined with the natural "recycling" of proteins in different tissues that can be used for breast development and function, or in bone function, increases the impact of prolactin signaling in breast cancer bone metastases. Thus, this review will focus on the role of prolactin in breast development, bone homeostasis and in breast cancer to bone metastases, covering the molecular aspects of the vicious cycle.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".