Prognostic significance of oligodendrocyte transcription factor 2 expression in glioma patients: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND Gliomas are the most common primary central nervous system neoplasm. Despite recent advances in the diagnosis and treatment of gliomas, patient prognosis remains dismal. Therefore, it is imperative to identify novel diagnostic biomarkers and therapeutic targets of glioma to effectively improve treatment outcomes. AIM To investigate the association between oligodendrocyte transcription factor 2 (Olig2) expression and the outcomes of glioma patients. METHODS The PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure databases were searched for studies (published up to October 2023) that investigated the relationship between Olig2 expression and prognosis of glioma patients. The quality of the studies was assessed using the Newcastle Ottawa Scale. Data analyses were performed using Stata Version 12.0 software. RESULTS A total of 1205 glioma patients from six studies were included in the meta-analysis. High Olig2 expression was associated with better outcomes in glioma patients [hazard ratio (HR): 0.81; 95% (confidence interval) CI: 0.51-1.27; P = 0.000]. Furthermore, the results of subgroup meta-analysis showed that high expression of Olig2 was associated with poor overall survival in European patients (HR: 1.34; 95%CI: 0.79-2.27) and better prognosis in Asian patients (HR: 0.43; 95%CI: 0.22-0.84). The sensitivity analysis showed that no single study had a significant effect on pooled HR, and there was also no indication of publication bias according to the Egger’s and Begger’s P value test or funnel plot test. CONCLUSION High Olig2 expression may have a positive impact on the prognosis of glioma patients, and should be investigated further as a prognostic biomarker and therapeutic target for glioma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".