Salivary exosomal microRNA profile as biomonitoring tool for diagnosis and prognosis of patients with head and neck squamous cell carcinoma: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Exosomes are extracellular vesicles found in saliva and other body fluids. These vesicles range in size from 30 to 150 nm and play a crucial role in intercellular communication, transporting different biomolecules, actively targeting cells. These vesicles regulate both physiological and pathological processes within recipient cells. MicroRNAs (miRs) are transported within exosomes and are delivered to target cells where they influence signaling pathways, taking on a crucial regulatory role in oncogenesis; for example, they are implicated in progression and infiltration of various cancers, such as head and neck squamous cell carcinoma (HNSCC). MATERIAL AND METHODS: A systematic literature search based on specific keywords, according to the PRISMA guidelines, was carried out on PubMed, Web of Science, Scopus, and Google Scholar. Only original articles were selected during this review. The risk of bias was assessed by QUADAS-2. RESULTS: At the end of the selection process 9 articles were included. In these studies, 41 miRs showed differential expression between healthy subjects and patient with HNSCC. The techniques varied among studies for the extraction and analysis of exosomal miRs. We presented also salivary exosomal miRs pathways, to give insights about pathogenetic mechanisms. CONCLUSIONS: Exosomal microRNA are promising biomarkers for HNSCC detection. MiR-10b-5p, miR-486-5p, miR-24-3p, miR-412-3p, and miR-512-3p are the most promising markers applicable to diagnostics, while miR-1307-5p and miR-519c-3p resulted overexpressed and correlated to worse survival outcomes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".