Progerin Inhibits the Proliferation and Migration of Melanoma Cells by Regulating the Expression of Paxillin
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Bibliographic record
Abstract
Objective: Progerin, the underlying cause of Hutchinson-Gilford Progeria Syndrome (HGPS), has been extensively studied for its impact on normal cells and premature aging patients. However, there is a lack of research on its specific effects on tumor cells. Melanoma is one of the most common malignant tumors with high morbidity and mortality. This study aimed to elucidate the potential therapeutic role of progerin in melanoma. Materials and Methods: We constructed the melanoma A375 cell line and M14 cell line with stable expression of progerin. The expression of progerin, paxillin, and epithelial-mesenchymal transition (EMT) marker proteins in each cell group was measured using Western blot. The migration, proliferation, and cell cycle of cancer cells were assessed using the transwell assay, wound healing assay, colony formation assay, CCK 8 assay, and flow cytometry. RT-qPCR technology was used to examine the impact of progerin overexpression on microRNA expression. Finally, we transfected paxillin into the progerin overexpression cell group to verify whether progerin regulates the phenotype of tumor cells through paxillin. Results: Our study demonstrated that overexpression of progerin leads to decreased expression of paxillin and inhibits cancer cell migration, proliferation, EMT process and cell cycle progression. Additionally, rescue experiments revealed that the migration, proliferation ability, and EMT marker protein expression in progerin overexpressing cancer cells could be partially restored by transfecting a plasmid containing the paxillin gene. Mechanistic investigations further revealed that progerin achieves this inhibition of paxillin expression by upregulating miR-212. Conclusion: This study reveals that progerin may inhibit the migration and proliferation of melanoma cells through the miR-212/paxillin axis, which provides a new approach for the future treatment of this disease.
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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