Circular RNAs are Potential Prognostic Markers of Head and Neck Squamous Cell Carcinoma: Findings of a Meta-Analysis Study
Bibliographic record
Abstract
Background Several studies have reported the role of circRNAs in the pathogenesis, diagnosis and prognosis of different cancers. This meta-analysis study aimed to evaluate the potential of using circRNAs as prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). Methods 816 relevant articles were retrieved from PubMed and Science Direct databases, out of which 17 met the inclusion criteria. These 17 studies were assessed for quality by the Newcastle-Ottawa Scale (NOS) system, and 9 high quality studies (NOS>7) were included in the meta-analysis. Cochran Q test and the I square (I2) metric were calculated to detect potential heterogeneity among studies. Sensitivity analysis was performed to validate the credibility of outcomes, and publication bias was determined using Begg’s funnel plot and Egger’s test. Hazard ratio (HR) and 95% Confidence Intervals (CIs) were used to evaluate overall survival (OS) of HNSCC patients by univariate and multivariate analyses. Results The dysregulated levels of 9 circRNAs (circPVT1, circCORO1C, circ_0000199, circCUX1, circPARD3, circMYC, circ_0102272, circ_0092125 and circ_00072387) were inversely related to OS of HNSCC patients [upregulated circRNA (univariate analysis: HR = 3.40, 95% CI: 2.66-4.36, p < 0.0001, I2 = 0%; multivariate analysis: HR = 3.33, 95% CI: 2.54-4.38, p < 0.0001, I2 = 0%), downregulated circRNA (univariate analysis: HR = 2.83, 95% CI: 1.73-4.65, p < 0.0001, I2 = 57.8%; multivariate analysis: HR = 2.35, 95% CI: 1.42-3.89, p = 0.0009, I2 = 0%)]. The individual HR for these 9 circRNAs indicated inverse relation to OS, validating the overall HR. The dyregulated levels of these circRNAs were also associated with poor clinicopathological outcomes such as primary tumor size, lymph node metastasis, distant metastasis and poor tumor (T), nodes (N), metastases (M); i.e TNM staging, and six of these circRNAs regulated diverse micro RNAs, revealing their role in tumorigenesis and cancer progression. Conclusion Nine different circRNAs dysregulated in HNSCC tumors may serve as potential prognostic markers of HNSCC. These markers are associated with reduced OS and poor clinicopathological outcomes of HNSCC patients. They are also involved in the pathogenesis and progression of HNSCC through diverse mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".