The role of <scp><i>MALAT1</i></scp> and <scp><i>UCA1</i></scp> long non‐coding <scp>RNAs</scp> on the prognosis of patients with glioblastoma: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Glioblastoma multiforme (GBM) is a common central nervous system malignancy with poor survival despite new treatments. Although some evidence demonstrated the prognostic effects of metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) and urothelial carcinoma associated 1 (UCA1) long non‐coding RNAs (lncRNAs) in patients with GBM, a comprehensive study has not yet evaluated the clinical importance of these lncRNAs. Hence, this review aimed to predict the significance of expressions of MALAT and UCA1 lncRNAs in patients with GBM. Using proper keywords, a thorough literature search was performed via databases, including PubMed, Web of Knowledge, Scopus, and EMBASE until December 2024. The relationship between lncRNA expressions and overall survival (OS) in patients with GBM was assessed using hazard ratios (HR) and confidence intervals (95% CI), and the fixed and random effects models were used to estimate the pooled effect size. Also, the Newcastle‐Ottawa Quality Assessment Scale was used as an appraisal tool. Among 1553 initially founded records, 13 studies were enrolled in the final analysis, consisting of 915 and 257 samples in the MALAT1 and UCA1 groups, respectively. Compared to the patients with low expression, those with high expression of MALAT1 had a mortality risk of 80% (HR = 1.8, 95% CI = [1.39, 2.33], p = .001). Additionally, the impact of UCA1 expression on patient prognosis indicated that lower OS among patients was correlated with high expression of UCA1; however, the meta‐analysis was not performed for UCA1 due to a lack of adequate studies. According to our findings, high expression of MALAT1 was correlated with poor prognosis in patients with GBM.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".