Leukemia Inhibitory Factor: A Newly Identified Metastatic Factor in Rhabdomyosarcomas
Bibliographic record
Abstract
Rhabdomyosarcoma frequently infiltrates bone marrow and this process involves the stromal-derived factor-1 (SDF-1)-CXCR4 axis. Because leukemia inhibitory factor (LIF), like SDF-1, is secreted by bone marrow stroma and directs the regeneration of skeletal muscles, we examined whether the LIF-LIF receptor (LIF-R) axis affects the biology of rhabdomyosarcoma cells. We found that in rhabdomyosarcoma cells, LIF stimulates the following: (a) phosphorylation of mitogen-activated protein kinase p42/44, AKT, and signal transducers and activators of transcription 3, (b) adhesion and chemotaxis, and (c) increased resistance to cytostatics. To compare the biological effects of LIF versus SDF-1, we examined the RH30 cell line, which is highly responsive to both ligands, and found that the chemotaxis of these cells is significantly reduced when the inhibitors of both receptors (T140 for CXCR4 and gp190 blocking antibody for LIF-R) are added simultaneously. Subsequently, by using repetitive chemotaxis to LIF or SDF-1, we selected from the RH30 line subpopulations of cells that respond to LIF but not SDF-1 (RH30-L) or to SDF-1 but not LIF (RH30-S). We found that (a) RH30-L cells seed better to the bone marrow, liver, and lymph nodes of immunodeficient mice than RH30-S cells and (b) mice inoculated i.m. with the RH30-L cells had more rhabdomyosarcoma cells in the bone marrow and lung after 6 weeks. Thus, we present the first evidence that the LIF-LIF-R axis may direct rhabdomyosarcoma metastasis. Further, because we showed that the in vivo metastasis of RH30 cells is inhibited by small interfering RNA against LIF-R, molecular targeting of this axis could become a new strategy to control the metastasis of rhabdomyosarcoma.
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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".