Homotypic RANK signaling differentially regulates proliferation, motility and cell survival in osteosarcoma and mammary epithelial cells
Bibliographic record
Abstract
RANKL (receptor activator of NF-κB ligand) is a crucial cytokine for regulating diverse biological systems such as innate immunity, bone homeostasis and mammary gland differentiation, operating through activation of its cognate receptor RANK. In these normal physiological processes, RANKL signals through paracrine and/or heterotypic mechanisms where its expression and function is tightly controlled. Numerous pathologies involve RANKL deregulation, such as bone loss, inflammatory diseases and cancer, and aberrant RANK expression has been reported in bone cancer. Here, we investigated the significance of RANK in tumor cells with a particular emphasis on homotypic signaling. We selected RANK-positive mouse osteosarcoma and RANK-negative preosteoblastic MC3T3-E1 cells and subjected them to loss- and gain-of-RANK function analyses. By examining a spectrum of tumorigenic properties, we demonstrate that RANK homotypic signaling has a negligible effect on cell proliferation, but promotes cell motility and anchorage-independent growth of osteosarcoma cells and preosteoblasts. By contrast, establishment of RANK signaling in non-tumorigenic mammary epithelial NMuMG cells promotes their proliferation and anchorage-independent growth, but not motility. Furthermore, RANK activation initiates multiple signaling pathways beyond its canonical target, NF-κB. Among these, biochemical inhibition reveals that Erk1/2 is dominant and crucial for the promotion of anchorage-independent survival and invasion of osteoblastic cells, as well as the proliferation of mammary epithelial cells. Thus, RANK signaling functionally contributes to key tumorigenic properties through a cell-autonomous homotypic mechanism. These data also identify the likely inherent differences between epithelial and mesenchymal cell responsiveness to RANK activation.
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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 | 0.000 |
| 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".