NF-κB downregulation may be involved the depression of tumor cell proliferation mediated by human mesenchymal stem cells
Bibliographic record
Abstract
AIM: It has been reported that stem cells are able to home to tumorigenesis and inhibit the proliferation of tumor cells. The purpose of our study was to demonstrate the molecular mechanism of the inhibitory proliferation of hepatoma cells and breast cancer cells mediated by human mesenchymal stem cells (hMSCs). METHODS: The proliferation of H7402 human hepatoma cells and MCF-7 human breast cancer cells was measured by the 5-bromodeoxyuridine (BrdU) incorporation assay and flow cytometry assay after the treatment with conditioned media from hMSCs culture, such as Z3 cells or BMMS-03 cells. The role of NF-kappaB or the phosphorylation of inhibitor kappaBalpha (p-IkappaBalpha) in the depression of hepatoma or breast cancer cells treated with conditioned media from Z3 cells or BMMS-03 cells was examined by reporter gene assay, quantitative real-time PCR, and Western blot analysis, respectively. RESULTS: The proliferation of H7402 cells and MCF-7 cells was decreased significantly by the BrdU incorporation assay and flow cytometry assay after treatment. The transcriptional activity and mRNA level of NF-kappaB were downregulated in the treated cells by reporter gene assay and quantitative real-time PCR in a dose-dependent manner. At the protein level, NF-kappaB and p-IkappaBalpha decreased in the treated cells by Western blot analysis. CONCLUSION: Conditioned media from hMSCs are able to inhibit the proliferation of tumor cells. NF-kappaB downregulation is one of reasons for the depression of tumor cell proliferation mediated by hMSCs.
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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 | 0.000 |
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".