Gelsolin regulates cisplatin sensitivity in human head-and-neck cancer
Bibliographic record
Abstract
Chemoresistance is a major challenge in cancer therapy. Cisplatin is commonly used for chemotherapy in patients with head-and-neck cancer (HNC), but it increases control of the disease by only 10-15%. Downregulation of proapoptotic pathways is a key determinant for chemoresistance in which gelsolin (GSN) is critically involved. We analyzed the association between GSN expression and cisplatin resistance in HNC cell lines, animals with HNC and cancer tissue samples from 58 cisplatin-treated patients with HNC. GSN expression levels were positively associated with chemoresistance in vitro and in vivo. Cisplatin-induced GSN downregulation was associated with the cleavage of GSN and the promotion of apoptosis. GSN silencing facilitated cisplatin-induced apoptosis in chemoresistant cells. In contrast, intact gelsolin was prosurvival in the presence of cisplatin by interacting with X-linked inhibitor of apoptosis protein (XIAP). In chemosensitive cells, cisplatin suppressed GSN-XIAP interaction, promoted translocation of XIAP from the perinuclear region to the nucleus and induced apoptosis. In chemoresistant cells, GSN was highly expressed, and cisplatin had no significant effect on GSN-XIAP interaction and apoptosis. We conclude that GSN is important for chemoresistance in HNC and may be an appropriate therapeutic target in chemoresistant cancers.
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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".