Comparison of miRNA Profiles of Primary Tumors and Metastatic Tumors of Salivary Gland Tumors and their Role in Prognosis: A Systematic Review
Bibliographic record
Abstract
Background: MicroRNAs (miRNAs) are implicated in several biological processes, such as control of tissue homeostasis, cell signaling, differentiation, proliferation, neoplastic transformation, and activation/inhibition of apoptotic mechanisms. In this systematic review, we evaluated the changes in the expression pattern of miRNAs in salivary gland tumors (SGTs). Methods: A comprehensive search was conducted in PubMed, and Scopus with no language and date restrictions in Feb 2023. All the studies on SGTs that evaluated miRNA profiling were included. Relevant data regarding the overexpression and down-regulation of the miRNAs were extracted. The quality of the included studies was evaluated with Newcastle-Ottawa checklist. The altered expression of miRNAs was evaluated between SGTs and normal cases, benign and malignant tumors, and primary and high-grade tumors. Results: Thirteen studies were included in this systematic review. There were considerable differences between malignant and benign tumors regarding the miRNAs expression level. In the five studies, the miRNA profile of the primary tumors was compared with metastatic tumors to reveal the involvement of the miRNA in the prognosis of the salivary tumors. The miRNAs expression changes were correlated with tumor size, stage, recurrence, and occurrence of solid components. Perineural invasion and lymph node metastasis were also reported in ACCLM cell line and recurrence of adenoid cystic carcinoma (ACC) tissues. Conclusion: The miRNA profiling confirms their prognostic value in salivary gland tumors. Significant alternations of the miRNAs expression are useful for distinguishing different types of salivary tumors and malignant tumors from benign types. The miRNA expression changes also affect the prognosis of salivary tumors.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".