Clinicopathological and Prognostic Significance of PRMT5 in Cancers: A System Review and Meta-Analysis
Bibliographic record
Abstract
PurposeSince protein arginine methyltransferase 5 (PRMT5) is abnormally expressed in various tumors, in this study we aim to assess the association between PRMT5 and clinicopathological and prognostic features.MethodsElectronic databases including PubMed, Web of Science, Scopus, ScienceDirect, and the Cochrane Library were searched until July 25, 2021. The critical appraisal of the eligible studies was performed using the Newcastle–Ottawa Quality Assessment Scale. Pooled hazard ratios (HR) and pooled odds ratios (OR) were calculated to assess the effect. Engauge Digitizer version 12.1, STATA version 15.1, and R version 4.0.5 were used to obtain and analysis the data.ResultsA total of 32 original studies covering 15,583 patients were included. In our data, it indicated that high level of PRMT5 was significantly correlated with advanced tumor stage (OR = 2.12, 95% CI: 1.22-3.70, P =.008; I2 = 80.7%) and positively correlated with poor overall survival (HR = 1.59, 95% CI: 1.46-1.73, P < .001; I2 = 50%) and progression-free survival (HR = 1.53, 95% CI: 1.24-1.88, P < .001; I2 = 0%). In addition, sub-group analysis showed that high level of PRMT5 was associated with poor overall survival for such 5 kinds of cancers as hepatocellular carcinoma, pancreatic cancer, breast cancer, gastric cancer, and lung cancer.ConclusionFor the first time we found PRMT5 was pan-cancerous as a prognostic biomarker and high level of PRMT5 was associated with poor prognosis for certain cancers.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".