MALAT1 expression in oral squamous cell carcinoma - A Systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: MALAT1 (Metastasis-Associated Lung Adenocarcinoma Transcript 1) is a long non-coding RNA that helps in disease prognosis. Objective: The aim of the study is to provide updated evidence on the expression rate of MALAT1 in oral squamous cell carcinoma (OSS) compared to normal cells and its other histopathological gradings, like well-differentiated, moderately differentiated, and poorly differentiated OSCC. Materials and Methods: The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in PROSPERO with the registration number CRD42024598836. A thorough search of databases was conducted from January 2000 to April 2024 to identify studies reporting the MALAT1 expression in OSCC cells compared to normal cells and its histological gradings of OSCC. Quality assessment was performed using the Newcastle Ottawa scale (NOS) for included studies. The standardized mean difference (SMD) was used for continuous outcomes, while odds ratio (OR) and risk ratio (RR) were applied for categorical outcomes, depending on the data reported. A random-effects model was used for all analyses, with statistical significance set at P < 0.05. Results: Seven studies qualified for inclusion, with four undergoing meta-analysis. Quality assessment indicated a moderate to low risk of bias. The meta-analysis revealed increased MALAT1 expression in OSCC cells (SMD = 3.90, 1.20-6.61) compared to normal tissue. Among OSCC grades, MALAT1 expression was higher in moderately differentiated OSCC than in well-differentiated (SMD = 5.50, –15.08–26.08), lower in moderately differentiated than in poorly differentiated (SMD = 10.50, –27.16–6.61), and lower in well-differentiated than in poorly differentiated OSCC (SMD = 1.50, –2.48–0.52). No publication bias was detected in the funnel plot. Conclusion: MALAT1 is a therapeutic factor in OSCC; its increased expression is related to OSCC growth and could help control metastasis, with overall good clinical relevance, making it a promising prognostic marker for OSCC.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".