Prognostic Significance of Nuclear Factor-κB p105/p50 in Human Melanoma and Its Role in Cell Migration
Bibliographic record
Abstract
Transcriptional factor nuclear factor-kappaB (NF-kappaB) family has been shown to play an important role in tumor pathogenesis and serve as a potential target in cancer therapy. However, it is necessary to clarify the specific functions of NF-kappaB members, which would provide the basis for the selective blockade and reduction of therapeutic side effects resulting from unspecific inhibition of NF-kappaB members. In this study, we explored the role of NF-kappaB p105/p50 in melanoma pathogenesis in vitro and in vivo. We found that the expression of NF-kappaB p105/p50 significantly increased in dysplastic nevi, primary melanoma, and metastatic melanoma compared with normal nevi (P = 0.0004, chi(2) test). Furthermore, NF-kappaB p105/p50 nuclear staining increased with melanoma progression and strong NF-kappaB p105/p50 nuclear staining was inversely correlated with disease-specific 5-year survival of patients with tumor thickness >2.0 mm (P = 0.014, log-rank test). Multivariate Cox regression analysis revealed that nuclear expression of NF-kappaB p105/p50 is an independent prognostic factor in this subgroup. Moreover, we found that up-regulation of NF-kappaB p50 enhanced melanoma cell migration, whereas small interfering RNA knockdown inhibited cell migration. In addition, overexpression of NF-kappaB p50 induced RhoA activity and Rock-mediated formation of stress fiber in melanoma cells. Taken together, our data indicate that NF-kappaB p105/p50 may be an important marker for human melanoma progression and prognosis as well as a potentially selective therapeutic target.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".