In vitro Anti-Tumor Effects of Statins on Head and Neck Squamous Cell Carcinoma: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Statins are commonly used against arteriosclerotic disease, but recent retrospective analyses have suggested that statins also prevent cancer. The aim of this systematic review is to verify the vitro anti-tumor effects of statins on head and neck squamous cell carcinoma. METHODS: Studies were gathered by searching Cochrane, MEDLINE, EMBASE, LILACS, and PubMed, up until May 9, 2015, with no time or language restrictions. Only in vitro studies that discuss the effect of statins on head and neck carcinoma were selected. RESULTS: Of 153 identified papers, 14 studies met the inclusion criteria. These studies demonstrated that statins had a significant effect on head and neck squamous cell carcinoma cell lines and influenced cell viability, cell cycle, cell death, and protein expression levels involved in pathways of carcinogenesis, which corroborates with the potential in vitro anti-tumor effects. It provides highlights about the biological mechanisms of statins used alone or associated with traditional therapy for cancer. CONCLUSIONS: Though there are few studies on the topic, currently available evidence suggests that statins shows that preclinical experiments supports the potentiality of statin as an adjuvant agent in chemotherapy and/or radiotherapy approaches routinely used in the management of HNSCC and should undergo further clinical assessment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".