ABCC5 supports osteoclast formation and promotes breast cancer metastasis to bone
Bibliographic record
Abstract
INTRODUCTION: Bone is the most common site of breast cancer metastasis, and complications associated with bone metastases can lead to a significantly decreased patient quality of life. Thus, it is essential to gain a better understanding of the molecular mechanisms that underlie the emergence and growth of breast cancer skeletal metastases. METHODS: To search for novel molecular mediators that influence breast cancer bone metastasis, we generated gene-expression profiles from laser-capture microdissected trephine biopsies of both breast cancer bone metastases and independent primary breast tumors that metastasized to bone. Bioinformatics analysis identified genes that are differentially expressed in breast cancer bone metastases compared with primary, bone-metastatic breast tumors. RESULTS: ABCC5, an ATP-dependent transporter, was found to be overexpressed in breast cancer osseous metastases relative to primary breast tumors. In addition, ABCC5 was significantly upregulated in human and mouse breast cancer cell lines with high bone-metastatic potential. Stable knockdown of ABCC5 substantially reduced bone metastatic burden and osteolytic bone destruction in mice. The decrease in osteolysis was further associated with diminished osteoclast numbers in vivo. Finally, conditioned media from breast cancer cells with reduced ABCC5 expression failed to induce in vitro osteoclastogenesis to the same extent as conditioned media from breast cancer cells expressing ABCC5. CONCLUSIONS: Our data suggest that ABCC5 functions as a mediator of breast cancer skeletal metastasis. ABCC5 expression in breast cancer cells is important for efficient osteoclast-mediated bone resorption. Hence, ABCC5 may be a potential therapeutic target for breast cancer bone metastasis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".