Dual Function of ERRα in Breast Cancer and Bone Metastasis Formation: Implication of VEGF and Osteoprotegerin
Bibliographic record
Abstract
Bone metastasis is a complication occurring in up to 70% of advanced breast cancer patients. The estrogen receptor-related receptor alpha (ERRα) has been implicated in breast cancer and bone development, prompting us to examine whether ERRα may function in promoting the osteolytic growth of breast cancer cells in bone. In a mouse xenograft model of metastatic human breast cancer, overexpression of wild-type ERRα reduced metastasis, whereas overexpression of a dominant negative mutant promoted metastasis. Osteoclasts were directly affected and ERRα upregulated the osteoclastogenesis inhibitor, osteoprotegerin (OPG), providing a direct mechanistic basis for understanding how ERRα reduced breast cancer cell growth in bone. In contrast, ERRα overexpression increased breast cancer cell growth in the mammary gland. ERRα-overexpressing primary tumors were highly vascularized, consistent with an observed upregulation of angiogenic growth factor, the VEGF. In support of these findings, we documented that elevated expression of ERRα mRNA in breast carcinomas was associated with high expression of OPG and VEGF and with disease progression. In conclusion, our results show that ERRα plays a dual role in breast cancer progression in promoting the local growth of tumor cells, but decreasing metastatic growth of osteolytic lesions in bone.
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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 | 0.000 | 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) | 0.002 | 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".