Reprogramming Factors Remodel Melanoma Cell Phenotype by Changing Stat3 Expression
Bibliographic record
Abstract
The limited availability of melanoma stem cells is a major challenge for therapeutic reagent screening and study of molecular mechanisms.It has been shown that induced expression of four stem cell factors (Oct4, Sox2, Klf4, and c-Myc) changes the phenotype of osteosarcoma and breast cancer cells to osteosarcoma stem cells and breast cancer stem cells, respectively.The present study aimed to explore whether these four factors might change the phenotype of melanoma cells to melanoma stem cells and, if so, to examine the possible molecular signal involved.Melanoma B16-F10 cells were transfected with the plasmid TetO-FUW-OSKM which contains cDNA expressing four factors, driven by the Tet-On element.We found that expression of the four transcription factors was highly induced by DOX in the stable melanoma cell clones.Further studies confirmed that induced expression of these factors remodeled the phenotype of the melanoma cells to melanoma stem cells (MSCs).This conclusion was supported by the evidence that induced expression of these factors increased the numbers of tumor-initiating cells, (namely MSCs), both in an in vitro cell culture system and in a mouse in vivo model.The conclusion was further supported by the observation that the induction of these factors exclusively increased the mRNA of signal transducer and activator of transcription 3 which has been reported to play a crucial role in stem cell maintenance.Thus, phenotypic remodeling of melanoma cells following the induction of these four factors provided a simple and optimal means to constantly obtain MSCs for screening new therapeutic reagents.The result also reveals that Stat3 may be a crucial link between the induction of the four factors and the cell remodeling, suggesting its potential role as a target to fight melanoma.
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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".