Prognostic Significance of Nuclear Factor-κB p105/p50 in Human Melanoma and Its Role in Cell Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it