Dysregulated circular RNAs as novel biomarkers in esophageal squamous cell carcinoma: a meta‐analysis
Bibliographic record
Abstract
Abstract Introduction Circular RNAs (circRNAs) play critical roles in tumorigenesis, but their clinical efficacy in esophageal squamous cell carcinoma (ESCC) still retains controversial. This meta‐analysis aims at evaluating the associations between circRNA expressions and clinicopathologic features as well as the diagnostic and prognostic values of circRNAs in ESCC. Materials & Methods PubMed, EMBASE, and other online databases were systematically searched to collect studies on circRNAs and clinicopathological features, diagnostic, and/or prognostic assessments of ESCC. The quality of included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS‐2) and Newcastle‐Ottawa Scale (NOS) scales. The included studies were quantitatively weighted and merged, and diagnostic indicators, hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) were calculated. P values were merged by Fisher᾽s method. Sources of heterogeneity were traced using subgroup, sensitivity, and meta‐regression analyses. Results As a result, 12 studies were included, representing 769 ESCC patients. The meta‐analysis showed that abnormal expressions of circRNAs were associated to TNM stage as well as lymph node and distant metastases in ESCC cases. CircRNA was used to distinguish ESCC patients from healthy controls, and the merged sensitivity, specificity, and the area under the curve (AUC) of ESCC were 0.78 (95% CI: 0.74–0.81), 0.79 (95% CI: 0.75–0.83), and 0.86, respectively. The survival analysis showed that upregulated oncogenic circRNA levels in ESCC tissues was associated with the shorter overall survival (OS) of the patients (univariate analysis: HR = 2.25, 95% CI: 1.71–2.95, p = 0.000, I 2 = 0.0%; multivariate analysis: HR = 2.50, 95% CI: 1.61–3.89, p = 0.000, I 2 = 0.0%), while the OS of ESCC patients presenting overexpressions of tumor‐suppressive circRNAs was significantly ameliorated (HR = 0.29, 95% CI: 0.20–0.42, p = 0.000, I 2 = 0.0%). The subgroup analyses based on circRNA biofunctions, sample size, and reference gene also revealed robust results. Conclusion CircRNAs can be used as promising molecular biomarkers for the early diagnosis and prognosis monitoring of ESCC.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".