Prognostic role of dysregulated circRNAs in patients with non-small cell lung cancer: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer incidence and mortality. Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancer, which lacks comprehensive prognostic biomarkers to predict the prognosis of patients. This research was performed to assess the potential prognostic role of circular RNAs (circRNAs) in patients with NSCLC. METHODS: We searched the following databases: PubMed, Web of Science, Embase, and Ovid MEDLINE(R) up to May 20, 2019 to identify studies which explored the association between circRNAs and NSCLC. Newcastle-Ottawa Scale (NOS) was applied to assess the quality of the included studies. Pooled hazard ratios (HRs) and the corresponding 95% confidence interval (CI) were calculated to assess the prognostic value of circRNAs in patients with NSCLC. Subgroup analyses were performed to explain heterogeneity among the included studies. Publication bias was estimated using Begg's funnel plot. Sensitivity analysis was performed to test the stability of pooled results. RESULTS: A total of 19 eligible studies including 1,650 NSCLC patients were included in this research. Pooled results indicated that the up-regulated expression of circRNAs was significantly associated with worse prognosis of patients with NSCLC (HR =2.08, 95% CI: 1.81-2.40). CONCLUSIONS: Our finding indicated that circRNAs could serve as prognostic biomarkers in patients with NSCLC. However, further large-scale prospective studies about the clinical significance of circRNAs are of great need in order to obtain conclusive results.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".