Vitamin D Regulates CXCL12/CXCR4 and Epithelial-to-Mesenchymal Transition in a Model of Breast Cancer Metastasis to Lung
Bibliographic record
Abstract
Vitamin D deficiency is associated with poor cancer outcome in humans, and administration of vitamin D or its analogs decreases tumor progression and metastasis in animal models. Using the mouse mammary tumor virus-polyoma middle T antigen (MMTV-PyMT) model of mammary cancer, we previously demonstrated a significant acceleration of carcinogenesis in animals on a low vitamin D diet and a reduction in spontaneous lung metastases when mice received vitamin D through perfusion. We investigate here the action mechanism for vitamin D in lung metastasis in the same non-immunodeficient model and demonstrate that it involves the control of epithelial to mesenchymal transition as well as interactions between chemokine C-X-C motif chemokine 12 (CXCL12) and its receptor C-X-C chemokine receptor type 4 (CXCR4). In vitro, 10-9M vitamin D treatment modifies the phenotype of MMTV-PyMT primary mammary tumor cells and significantly decreases their invasiveness and mammosphere formation capacity by 40% and 50%, respectively. Vitamin D treatment also inhibits phospho-signal transducer and activator of transcription 3 (p-STAT3), zinc finger E-box-binding homeobox 1 (Zeb1), and vimentin by 52%, 75%, and 77%, respectively, and increases E-cadherin by 87%. In vivo, dietary vitamin D deficiency maintains high levels of Zeb1 and p-STAT3 in cells from primary mammary tumors and increases CXCL12 expression in lung stroma by 64%. In lung metastases, vitamin D deficiency increases CXCL12/CXCR4 co-localization by a factor of 2.5. These findings indicate an involvement of vitamin D in mammary cancer "seed" (primary tumor cell) and "soil" (metastatic site) and link vitamin D deficiency to epithelial-to-mesenchymal transition (EMT), CXCL12/CXCR4 signaling, and accelerated metastasis, suggesting vitamin D repleteness in breast cancer patients could enhance the efficacy of co-administered therapies in preventing spread to distant organs.
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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 | 0.001 | 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 | 0.001 |
| 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 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".