Molecular Pathways: Epigenetic Modulation of Wnt–Glycogen Synthase Kinase-3 Signaling to Target Human Cancer Stem Cells
Bibliographic record
Abstract
Aberrant regulation of the canonical Wnt signaling pathway (Wnt-β-catenin-GSK3 axis) has been a prevalent theme in cancer biology since earlier observations until recent genetic discoveries gleaned from tumor genome sequencing. During the last few decades, a large body of work demonstrated the involvement of the Wnt-β-catenin-GSK3 signaling axis in the formation and maintenance of cancer stem cells (CSC) responsible for tumor growth in several types of human malignancies. Recent studies have elucidated epigenetic mechanisms that control pluripotency and stemness, and allow a first assessment on how embryonic and normal tissue stem cells are dysregulated in cancer to give rise to CSCs, and how canonical Wnt signaling might be involved. Here, we review emerging concepts highlighting the critical role of epigenetics in CSC development through abnormal canonical Wnt signaling. Finally, we refer to the characterization of novel and powerful inhibitors of chromatin organization machinery that, in turn, restore the Wnt-β-catenin-GSK3 signaling axis in malignant cells, and describe attempts/relevance to bring these compounds into preclinical and clinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".