Myeloma cells inhibit osteogenic differentiation of mesenchymal stem cells and kill osteoblasts via TRAIL-induced apoptosis
Bibliographic record
Abstract
INTRODUCTION: Myeloma bone disease (MBD) is the result of the increased activity of osteoclasts (OCs), which is not accompanied by a comparable increase of osteoblast (OB) function, thus leading to enhanced bone resorption. Osteoblasts can also regulate osteoclast activity through expression of cytokines, such as receptor activator of nuclear factor-κB ligand (RANKL), which activates osteoclast differentiation, and osteoprotegerin (OPG), which inhibits RANKL by acting as a decoy receptor. MATERIAL AND METHODS: Based on a series of 21 patients with multiple myeloma (MM) and human osteoblast cell line HFOB1.19, we provide evidence that the bone marrow-derived mesenchymal stem cells (BMMSCs) of patients with MM exhibit normal phenotype, but showed reduced efficiency to differentiate into OBs as compared with normal controls. RESULTS: In vitro assays showed that MM cells inhibited the potential of osteogenic differentiation of BMMSCs from healthy controls and rendered the OBs sensitive to TRAIL-induced apoptosis. There was no evidence of the formation of tartrate-resistant acid phosphatase positive OCs. The osteogenic differentiation of HFOB1.19 was also inhibited in the presence of RPMI 8266 or XG7 MM cells, as confirmed by von Kossa and ALP staining. Osteoblast s induced from BMMSCs supported survival and proliferation of MM cells, especially when the MM cells were cultured in medium containing rhTRAIL and dexamethasone. Multiple myeloma cells proliferated and grew well in the presence of residual OBs. CONCLUSIONS: Besides OCs, our results demonstrated that OBs and MM cells were dependent upon each other and made a microenvironment suitable for MM cells.
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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 | 0.001 |
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".