Progerin regulates actin cytoskeletal remodeling and inhibits EMT and metastasis in triple‑negative breast cancer cells
Bibliographic record
Abstract
Triple‑negative breast cancer (TNBC) is a subtype of breast cancer, known for its poor prognosis due to its high invasiveness, strong metastatic tendencies and propensity for recurrence. Epithelial to mesenchymal transition (EMT) is a crucial process in tumor invasion and metastasis and in the formation of cancer‑initiating cells. Hutchinson‑Gilford progeria is a rare condition characterized by accelerated aging, caused by a mutated form of lamin A, known as progerin. The present study aimed to investigate the effect of progerin overexpression on TNBC and uncover its underlying mechanisms of action. Therefore, cell senescence was assessed using senescence‑associated β‑galactosidase staining, while cell proliferation was measured by colony formation, Cell Counting Kit‑8 and EdU assays. Additionally, cell metastasis was evaluated using wound‑healing, Transwell and cell adhesion assays. Immunofluorescence staining was carried out to observe actin cytoskeleton and nuclear morphology. The results showed that progerin markedly suppressed the colony formation, migration, invasion and adhesion abilities of BT‑549 and MDA‑MB‑231 TNBC cell lines, without affecting cell senescence or proliferation. In addition, progerin overexpression altered nuclear morphology and actin cytoskeleton organization in TNBC cells. Furthermore, the expression levels of the mesenchymal markers, N‑cadherin, vimentin, Snail and Slug, were reduced, while those of the epithelial marker, E‑cadherin, were enhanced in TNBC cells. Overall, the results of the present study suggested that progerin overexpression could inhibit TNBC cell metastasis, probably via actin cytoskeleton remodeling and regulate the expression levels of the cytoskeletal‑related proteins, anillin and β‑catenin, and those of the EMT‑related ones. The aforementioned findings could provide novel insights into the identification of potential molecular targets for breast cancer therapy.
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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 | 0.000 | 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) | 0.001 | 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".