The PKC/NF-κB Signaling Pathway Induces APOBEC3B Expression in Multiple Human Cancers
Bibliographic record
Abstract
Overexpression of the antiviral DNA cytosine deaminase APOBEC3B has been linked to somatic mutagenesis in many cancers. Human papillomavirus infection accounts for APOBEC3B upregulation in cervical and head/neck cancers, but the mechanisms underlying nonviral malignancies are unclear. In this study, we investigated the signal transduction pathways responsible for APOBEC3B upregulation. Activation of protein kinase C (PKC) by the diacylglycerol mimic phorbol-myristic acid resulted in specific and dose-responsive increases in APOBEC3B expression and activity, which could then be strongly suppressed by PKC or NF-κB inhibition. PKC activation caused the recruitment of RELB, but not RELA, to the APOBEC3B promoter, implicating noncanonical NF-κB signaling. Notably, PKC was required for APOBEC3B upregulation in cancer cell lines derived from multiple tumor types. By revealing how APOBEC3B is upregulated in many cancers, our findings suggest that PKC and NF-κB inhibitors may be repositioned to suppress cancer mutagenesis, dampen tumor evolution, and decrease the probability of adverse outcomes, such as drug resistance and metastasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".