Predictive Prognostic Value of Tissue-Based MicroRNA Expression in Oral Squamous Cell Carcinoma: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Oral squamous cell carcinoma (OSCC) is a common type of cancer characterized by a low survival rate, mostly due to local recurrence and metastasis. In view of the importance of predicting tumor behavior in the choice of treatment strategies for OSCC, several studies have attempted to investigate the prognostic value of tissue biomarkers, including microRNA (miRNA). The purpose of this study was to perform a systematic review and meta-analysis to evaluate the relationship between miRNA expression and survival of OSCC patients. Studies were identified by searching on MEDLINE/PubMed, SCOPUS, Web of Science, and Google Scholar. Quality assessment of studies was performed with the Newcastle-Ottawa Scale. Data were collected from cohort studies comparing disease-free survival and overall survival in patients with high miRNA expression compared to those with low expression. A total of 15 studies featuring 1,200 OSCC samples, predominantly from Asia, met the inclusion criteria and were included in the meta-analysis. Poor prognosis correlated with upregulation of 9 miRNAs (miR-21, miR-455-5p, miiR-155-5p, miR-372, miR-373, miR-29b, miR-1246, miR-196a, and miR-181) and downregulation of 7 miRNAs (miR-204, miR-101, miR-32, miR-20a, miR-16, miR-17, and miR-125b). The pooled hazard ratio values (95% confidence interval) related to different miRNA expression for overall survival and disease-free survival were 2.65 (2.07-3.39) and 1.95 (1.28-2.98), respectively. The results of this meta-analysis revealed that the expression levels of specific miRNAs can robustly predict prognosis of OSCC patients.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".