MIP-1α (CCL3) is a downstream target of FGFR3 and RAS-MAPK signaling in multiple myeloma
Bibliographic record
Abstract
Overexpression of fibroblast growth factor receptor 3 (FGFR3) is a hallmark of t(4;14) multiple myeloma (MM). To dissect the mechanism of FGFR3 oncogenesis in MM, we used 3 FGFR selective kinase inhibitors-CHIR258, PD173074, and SU5402-and FGFR3-specific siRNA to modulate FGFR3 activity. Conversely, the ligand FGF was used to stimulate FGFR3 function in human MM cells. The transcriptional response to FGFR3 modification was recorded, and gene expression changes common to all 5 modifiers were documented. Ten genes were commonly regulated. Macrophage inflammatory protein-1 alpha (MIP-1alpha) was the single most differentially altered gene. MIP-1 alpha promoter function, gene expression, and protein secretion were each down-regulated following inhibition of FGFR3 signaling. Down-regulation of MIP-1 alpha was not, however, observed following FGFR3 inhibition in MM cells with RAS mutations implicating RAS-MAPK in MIP-1 alpha regulation. As confirmation, inhibition of ERK1 also down-regulated MIP-1 alpha in FGFR3 inhibitor-resistant cells harboring RAS mutations. MIP-1 alpha is implicated in the survival and proliferation of MM cells and the pathogenesis of MM bone disease. Our observation is the first to directly link an initiating IgH translocation not only to MM-cell growth and survival but also to the disease-associated bone disease.
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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".