CircRNAs as potential biomarkers for the clinicopathology and prognosis of glioma patients: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: An increasing number of studies have reported circular RNAs (circRNAs) as new potential biomarkers for the prognosis of gliomas. However, the overall prognostic value of circRNAs for glioma remains unclear. Therefore, this study is the first comprehensive evaluation of the clinicopathological and prognostic value of dysregulated circRNAs in the treatment of glioma patients. METHODS: We systematically reviewed the online databases of PubMed, Web of Science, EMBASE, and Cochrane Library to identify studies that explored the relationship between circRNA expression and clinicopathological and prognostic factors in glioma through April 11, 2020. The quality of the included studies was evaluated by the Newcastle-Ottawa Scale (NOS) checklists. Clinicopathological features were assessed by pooled odds ratios (ORs) and 95% confidence intervals (CIs), and overall survival (OS) was assessed by hazard ratios (HRs) and 95% CIs. RESULTS: Twenty-four eligible studies, including 22 studies of clinicopathological features, 1 diagnostic study, and 18 studies of prognosis, that included a total of 1390 patients were ultimately included in this study. Meta-analysis showed that highly expressed oncogenic circRNAs were significantly related to poor clinicopathological features (age: P = 0.026; tumor size: P ≤ 0.001; tumor grade: P ≤ 0.001; KPS: P = 0.012) and worse overall survival (OS) (HR = 2.01, 95% CI: 1.61-2.50, P ≤ 0.001). Moreover, we found that highly expressed tumor-suppressor circRNAs were related to better clinicopathological features (gender: P = 0.042; age: P = 0.014; tumor size: P = 0.022; tumor grade: P ≤ 0.001) and longer OS (HR = 2.70, 95% CI: 1.82-3.99, P ≤ 0.001). CONCLUSIONS: In conclusion, there is a significant correlation between the dysregulated expression of circRNAs and the clinicopathology and prognosis of glioma patients.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.037 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".