Master Regulators of Epithelial-Mesenchymal Transition and WNT Signaling Pathways in Juvenile Nasopharyngeal Angiofibromas
Bibliographic record
Abstract
Juvenile nasopharyngeal angiofibroma (JNA) is a rare fibrovascular benign tumor showing an invasive growth pattern and affecting mainly male adolescents. We investigated the role of epithelial–mesenchymal transition (EMT) and WNT signaling pathways in JNA. Gene expression profiles using nine JNA paired with four inferior nasal turbinate samples were interrogated using a customized 2.3K microarray platform containing genes mainly involved in EMT and WNT/PI3K pathways. The expression of selected genes (BCL2, CAV1, CD74, COL4A2, FZD7, ING1, LAMB1, and RAC2) and proteins (BCL2, CAV1, CD74, FZD7, RAF1, WNT5A, and WNT5B) was investigated by RT-qPCR (28 cases) and immunohistochemistry (40 cases), respectively. Among 104 differentially expressed genes, we found a significantly increased expression of COL4A2 and LAMB1 and a decreased expression of BCL2 and RAC2 by RT-qPCR. The immunohistochemistry analysis revealed a low expression of BCL2 and a negative to moderate expression of FZD7 in most samples, while increased CAV1 and RAF1 expression were detected. Moderate to strong CD74 protein expression was observed in endothelial and inflammatory cells. A significant number of JNAs (78%) presented reduced WNT5A and increased WNT5B expression. Overall, the transcript and protein profile indicated the involvement of EMT and WNT pathways in JNA. These candidates are promising druggable targets for treating JNA.
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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 | 0.000 |
| 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 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".