Targeting Calcium Signaling Induces Epigenetic Reactivation of Tumor Suppressor Genes in Cancer
Bibliographic record
Abstract
Targeting epigenetic pathways is a promising approach for cancer therapy. Here, we report on the unexpected finding that targeting calcium signaling can reverse epigenetic silencing of tumor suppressor genes (TSG). In a screen for drugs that reactivate silenced gene expression in colon cancer cells, we found three classical epigenetic targeted drugs (DNA methylation and histone deacetylase inhibitors) and 11 other drugs that induced methylated and silenced CpG island promoters driving a reporter gene (GFP) as well as endogenous TSGs in multiple cancer cell lines. These newly identified drugs, most prominently cardiac glycosides, did not change DNA methylation locally or histone modifications globally. Instead, all 11 drugs altered calcium signaling and triggered calcium-calmodulin kinase (CamK) activity, leading to MeCP2 nuclear exclusion. Blocking CamK activity abolished gene reactivation and cancer cell killing by these drugs, showing that triggering calcium fluxes is an essential component of their epigenetic mechanism of action. Our data identify calcium signaling as a new pathway that can be targeted to reactivate TSGs in cancer.
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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".