Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells
Bibliographic record
Abstract
Growing evidence supports the existence of a subpopulation of cancer cells with stem cell characteristics within breast tumors. In spite of its potential clinical implications, an understanding of the mechanisms responsible for retaining the stem cell characteristics in these cells is still lacking. Here, we used the mammosphere model combined with DNA methylation bead arrays and quantitative gene expression to characterize the epigenetic mechanisms involved in the regulation of developmental pathways in putative breast cancer stem cells. Our results revealed that MCF7-derived mammospheres exhibit distinct CpG promoter methylation profiles in a specific set of genes, including those involved in Jak-STAT signaling pathway. Hypomethylation of several gene components of the Jak-STAT pathway was correlated with an increased expression in mammospheres relative to parental cells. Remarkably, cell sorting of the cells with a putative cancer stem cell phenotype (CD44+/CD24 low) suggests a constitutive activation of Jak-STAT pathway in these cells. These results show that Jak-STAT activation may represent a characteristic of putative breast cancer stem cells. In addition, they favor the concept that the expression of cancer stem-like pathways and the establishment and maintenance of defining properties of cancer stem cells are orchestrated by epigenetic mechanisms.
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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".