METHYLATION OF TUMOR SUPPRESSOR GENES IN BENIGN AND MALIGNANT SALIVARY GLAND TUMORS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
The aim of the present systematic review was to critically analyze the relationship of tumor suppressor gene (TSGs) promoter methylation, a potent mechanism of gene silencing, and the development of salivary gland tumors, as well as the possible effect on clinical/histological characteristics. Review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (registration ID CRD42020218511). A comprehensive search of Web of Science, Scopus, PubMed and Cochrane Central Register of Controlled Trials was performed utilizing relevant key terms, supplemented by a search of gray literature. Newcastle-Ottawa Quality Assessment Scale (NOQAS) was used for the quality assessment of included studies. Sixteen cross-sectional and twelve case-control studies were included in the review, predominantly dealing with methylation in TSGs related to DNA repair, cell cycle and cell growth regulation and differentiation. Quantitative synthesis could be performed on P16 (inhibitor of cyclin-dependent kinase 4a), RASSF1A (Ras association domain family 1 isoform A) and MGMT (O6-methylguanine DNA methyltransferase) genes only. It showed that P16 and RASSF1A genes were more frequently methylated in salivary gland tumors compared to controls (P = .0002 and P < .0001, respectively), while no significant difference was observed for MGMT. Additionally, P16 did not appear to be related to malignant transformation of pleomorphic adenomas (P = .330). In conclusion, TSG methylation is involved in salivary gland tumor pathogenesis and several genes might play a considerable role. Further studies are needed for a better understanding of complex epigenetic deregulation during salivary gland tumor development and progression.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".