Human Mesenchymal Stem Cells-mediated Transcriptomic Regulation of Leukemic Cells in Delivering Anti-tumorigenic Effects
Bibliographic record
Abstract
Treatment of leukemia has become much difficult because of resistance to the existing anticancer therapies. This has thus expedited the search for alternativ therapies, and one of these is the exploitation of mesenchymal stem cells (MSCs) towards control of tumor cells. The present study investigated the effect of human umbilical cord-derived MSCs (UC-MSCs) on the proliferation of leukemic cells and gauged the transcriptomic modulation and the signaling pathways potentially affected by UC-MSCs. The inhibition of growth of leukemic tumor cell lines was assessed by proliferation assays, apoptosis and cell cycle analysis. BV173 and HL-60 cells were further analyzed using microarray gene expression profiling. The microarray results were validated by RT-qPCR and western blot assay for the corresponding expression of genes and proteins. The UC-MSCs attenuated leukemic cell viability and proliferation in a dose-dependent manner without inducing apoptosis. Cell cycle analysis revealed that the growth of tumor cells was arrested at the G 0 /G 1 phase. The microarray results identified that HL-60 and BV173 share 35 differentially expressed genes (DEGs) (same expression direction) in the presence of UC-MSCs. In silico analysis of these selected DEGs indicated a significant influence in the cell cycle and cell cycle-related biological processes and signaling pathways. Among these, the expression of DBF4, MDM2, CCNE2, CDK6, CDKN1A, and CDKN2A was implicated in six different signaling pathways that play a pivotal role in the anti-tumorigenic activity exerted by UC-MSCs. The UC-MSCs perturbate the cell cycle process of leukemic cells via dysregulation of tumor suppressor and oncogene expression.
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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".