Nuclear factor kappa B subunit p50 promotes melanoma angiogenesis by upregulating interleukin‐6 expression
Bibliographic record
Abstract
Nuclear factor kappa B (NF-kappaB) signaling is deregulated in many tumor types, resulting in aberrant expression and/or activation of NF-kappaB transcriptional complexes. We have previously reported that nuclear expression of the NF-kappaB subunit p50 is strongly correlated with melanoma progression and poor 5-year patient survival. In this study, we used cDNA microarray to analyze the gene expression profiles of melanoma cells overexpressing NF-kappaB p50. We found that NF-kappaB p50 expression strongly induced interleukin-6 (IL-6) upregulation in melanoma cells at both the transcriptional and translational levels and that IL-6 production by melanoma cells enhanced the growth of endothelial cells in vitro. Expression of activating transcription factor 3 (ATF3), a negative regulator of IL-6 gene transcription, inhibited p50-mediated IL-6 upregulation. Knockdown of p50 expression using lentiviral-based shRNA abrogated IL-6 induction in melanoma cells and inhibited its effects on endothelial cell growth. Finally, we used an in vivo matrigel plug assay to show that NF-kappaB p50 overexpression promotes angiogenesis, while silencing NF-kappaB p50 inhibits blood vessel formation. Our results demonstrate for the first time that the NF-kappaB p50 subunit mediates melanoma angiogenesis by specifically upregulating IL-6, highlighting a novel and important role for the NF-kappaB p50/IL-6 signaling axis in melanoma progression.
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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) | 0.002 | 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".