Implications of STAT3 and STAT5 signaling on gene regulation and chromatin remodeling in hematopoietic cancer
Bibliographic record
Abstract
STAT3 and STAT5 proteins are oncogenic downstream mediators of the JAK-STAT pathway. Deregulated STAT3 and STAT5 signaling promotes cancer cell proliferation and survival in conjunction with other core cancer pathways. Nuclear phosphorylated STAT3 and STAT5 regulate cell-type-specific transcription profiles via binding to promoter elements and exert more complex functions involving interaction with various transcriptional coactivators or corepressors and chromatin remodeling proteins. The JAK-STAT pathway can rapidly reshape the chromatin landscape upon cytokine, hormone, or growth factor stimulation and unphosphorylated STAT proteins also appear to be functional with respect to regulating chromatin accessibility. Notably, cancer genome landscape studies have implicated mutations in various epigenetic modifiers as well as the JAK-STAT pathway as underlying causes of many cancers, particularly acute leukemia and lymphomas. However, it is incompletely understood how mutations within these pathways can interact and synergize to promote cancer. We summarize the current knowledge of oncogenic STAT3 and STAT5 functions downstream of cytokine signaling and provide details on prerequisites for DNA binding and gene transcription. We also discuss key interactions of STAT3 and STAT5 with chromatin remodeling factors such as DNA methyltransferases, histone modifiers, cofactors, corepressors, and other transcription factors.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".