Profilin 2 Promotes Proliferation and Metastasis of Head and Neck Cancer Cells by Regulating PI3K/AKT/β-Catenin Signaling Pathway
Bibliographic record
Abstract
Profilin 2 (PFN2) was found to be mainly expressed in neurons and involved in the development of the brain. In recent years, emerging evidence indicated that PFN2 is also significantly upregulated in various cancers including head and neck cancer (HNSC) and influences cancer cell proliferation, migration, and invasion. However, the role of PFN2 in HNSC development and progression remains unclear. The aim of our study was to investigate the role of PFN2 in the development of HNSC and its possible molecular mechanisms. Bioinformatics showed that increased expression of PFN2 in tumors correlated highly with poor prognosis of HNSC patients. Our results indicated that PFN2 was highly expressed in HNSC tissues and in HNSC cell lines. Knockdown of PFN2 inhibited proliferation, invasion, and migration of HNSC cells, while PFN2 overexpression produced the opposite effects. Using a nude mouse xenograft model, we substantiated the tumor-promoting effect of PFN2 on HNSC in vivo. Furthermore, we found that PFN2 downregulation reduced the phosphorylation of Akt and GSK-3β and reduced the expression of β-catenin in HNSC cells. The opposite was observed when PFN2 was overexpressed. Collectively, these results suggest that PFN2 promotes the proliferation and metastasis of HNSC by activating the PI3K/Akt/β-catenin signaling pathway. Although further validation is needed, we speculate that PFN2 plays a crucial role in HNSC and may be a promising therapeutic target and prognostic biomarker.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".