JWA inhibits melanoma angiogenesis by suppressing ILK signaling and is an independent prognostic biomarker for melanoma
Bibliographic record
Abstract
Melanoma is the deadliest cutaneous malignancy because of its high incidence of metastasis. Melanoma growth and metastasis relies on sustained angiogenesis; therefore, inhibiting angiogenesis is a promising approach to treat metastatic melanoma. JWA is a novel microtubule-associated protein and our previous work revealed that JWA inhibited melanoma cell invasion and metastasis. However, the role of JWA in melanoma angiogenesis and the prognostic value are still unknown. Here, we report that JWA in melanoma cells significantly inhibited the tube formation of endothelial cells. In addition, JWA regulated integrin-linked kinase (ILK) through integrin αVβ3 and such regulation was achieved through the transcription factor Sp1. Notably, both in vitro and in vivo angiogenesis assays revealed that JWA dramatically suppressed melanoma angiogenesis by inhibiting ILK signaling. Furthermore, we examined the expression of JWA protein in a large set of melanocytic lesions (n = 505) at different stages by tissue microarray and found an inverse correlation between JWA expression and melanoma progression (P = 5 × 10(-6)). Importantly, reduced JWA expression was correlated with a poorer overall, and disease-specific 5 year survival of patients (P = 0.001 and 0.007, respectively). Multivariate Cox regression analyses indicated that JWA was an independent prognostic marker for melanoma patients. Moreover, we found a significant negative correlation between JWA and ILK in melanoma biopsies, and their concomitant expression was closely correlated with melanoma patient survival (P = 0.004), further indicating the regulation of ILK expression by JWA is critical in melanoma. Taken together, our data highlight the function of JWA in melanoma angiogenesis and reveal the clinical prognostic value of JWA.
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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 | 0.001 | 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 | 0.001 |
| 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".