Circular RNA circSMARCA5 is a prognostic biomarker in patients with malignant tumor: a meta-analysis
Bibliographic record
Abstract
Abstract Background Malignant tumor is one of the most serious diseases endangering human health. Circular RNAs play an important role in the tumorigenesis and progression of various malignant tumors. Although various studies have investigated the biological function of circular RNA circSMARCA5 in malignant tumors, the prognostic value of circSMARCA5 in malignant tumor patients has not been systematically analyzed. Methods Relevant studies were obtained from the PubMed and Web of Science database. The quality of the enrolled studies was evaluated using the Newcastle-Ottawa Scale quality assessment system. Survival features and clinicopathological features were assessed using pooled hazard ratios and odds ratios with 95% confidence intervals, respectively. Results Overall, 7 relevant publications were enrolled in the meta-analysis. CircSMARCA5 expression was significantly correlated with better OS (HR = 0.51, 95%CI 0.41–0.65) or DFS/RFS/PFS (HR = 0.56, 95%CI 0.43–0.73) in malignant tumors. In the pooled analyses of clinicopathological characteristics, malignant tumors with higher circSMARCA5 were better differentiated (OR = 0.41, 95%CI 0.19–0.88). CircSMARCA5 expression was correlated with less advanced TNM stage (OR = 0.33, 95%CI 0.19–0.55). Moreover, malignant tumors with higher circSMARCA5 expression have less advanced lymph node metastasis (OR = 0.26, 95%CI 0.08–0.79). Conclusion These results indicated that circSMARCA5 was a promising biomarker in malignant tumors, which may potentially facilitate clinical decisions in the future.
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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) | 0.856 | 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".