Effect of PIMREG Expression on Prognosis in Glioma Patients: A Meta-Analysis
Bibliographic record
Abstract
Background: The phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG) is highly expressed in osteosarcoma, cholangiocarcinoma, breast cancer, pancreatic cancer, and other cancer types, with its high expression being associated with poor cancer survival. At the same time, some studies have explored the association between PIMREG expression and glioma, but the results are controversial. Therefore, this study aimed to conduct a meta-analysis to systematically evaluate the effect of PIMREG expression on the prognosis of glioma patients. Methods: The relevant literature published in English was accessed through various databases, including PubMed, Embase, Web of Science, and The Cochrane Library from September 2023. The research articles were screened based on the predetermined inclusion and exclusion criteria. The quality of the literature was assessed using the Newcastle-Ottawa Scale (NOS). Furthermore, hazard ratio (HR) and its corresponding 95% confidence interval (CI) for overall survival (OS) were either directly obtained from the original sources or derived from the Kaplan-Meier survival graphs using Engauge Digitizer 4.3. STATA 15.0 was selected for meta-analysis. Moreover, sensitivity analysis was performed to evaluate the stability of the included studies. Additionally, the Begg rank correlation method and Egger regression method were employed to evaluate the publication bias of the included literature. Results: Following a thorough screening process, 6 research articles were included in this study. Meta-analysis results showed that patients with high PIMREG expression had poorer OS (HR = 2.77, 95% CI: 1.83–3.71). The subgroup analysis revealed that the HR of OS was 2.32 (95% CI: 1.59–3.06) in the Asian population and 3.12 (95% CI: 0.80–5.44) in the non-Asian population. In subgroups of tumor types, the HR for OS was 2.71 (95% CI: 1.88–3.54) for patients with glioma type and 2.60 (95% CI: 0.87–4.34) for those with glioblastoma type. Furthermore, sensitivity analysis revealed that the stability of the included studies was good. Begg and Egger tests showed that in the meta-analysis, publication bias of the included literature was not significant ( p = 0.630). Conclusion: The high expression of PIMREG is associated with poor prognosis of glioma patients, indicating its application as a potential prognostic indicator for these patients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.043 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".