Table 1.
Comprehensive demographic information of the patients.
Table 2.
The p value and AUC value of 36 differentially expressed URGs.
Fig 1.
Identification of differentially expressed ubiquitination related genes (URGs) related to prognosis.
Through joint analysis of TCGA (Including 697 specimens of glioma and 5 cases of non-tumor brain tissue) and GTEx (Containing 1152 healthy brain specimens before death) datasets, we identified 25 URGs related to survival. The line with a green dot indicates low risk factors, and the line with a red dot indicates high risk factors. P<0.05 are statistically significant.
Fig 2.
Survival curves of URGs related to the prognosis of patients.
Survival analysis obtained URGs related to patient prognosis. Here we only showed the genes that also satisfy the true positive rate greater than 0.7 in ROC curve analysis: CDC20 (A), UBE2C (B), WDR62 (C), DTL (D), HOXB4 (E) and TRIM38 (F).
Fig 3.
ROC curves for URGs related to patient prognosis.
ROC curve analysis identified 6 genes with AUC greater than 0.7: CDC20 (A), UBE2C (B), WDR62 (C), DTL (D), HOXB4 (E) and TRIM38 (F).
Fig 4.
Cox analysis of the relationship of URGs with glioma patient prognosis.
(A) Univariate Cox analysis found that except for gender and mutation count, they were all related to patient prognosis. (B) Multivariate Cox analysis found that only age, IDH status, and Chr 19/20 co-gain were independent factors for patient prognosis. The line with a green dot indicates low risk factors, and the line with a red dot indicates high risk factors.
Table 3.
Univariate analysis and multivariate analysis of the correlation of the expression of CDC20, UBE2C, WDR62, DTL, HOXB4 and TRIM38 with OS among glioma patients.
Fig 5.
Patient prognosis based on the model constructed using URGs.
(A) Patient prognosis risk score and distribution. (B) The survival time and status of patients with different risk scores. (C) Heatmap of URG expression profiles.
Fig 6.
The prognostic value of the risk model.
(A) Number of events and patient survival rate in high- and low-risk groups over time. (B) ROC curve analysis of the accuracy of the risk model. The true positive rate was 0.853.
Fig 7.
Analysis of risk model in predicting patient prognosis.
(A) Univariate Cox analysis of the value of common clinical molecular markers and the risk model in predicting patient prognosis. Gender and mutation count showed no statistical significance with patient prognosis. (B) Multivariate Cox analysis of common clinical molecular markers and the risk model as independent prognostic factors. Age, grade, IDH status, Chr 19/20 co-gain and the risk model were independent prognostic factors. Parameters with green dots indicate low risk factors, and those with red dots indicate high risk factors.
Table 4.
A model for predicting patient prognosis constructed with URGs.
Table 5.
Univariate analysis and multivariate analysis of the correlation of the expression of riskScore with OS among glioma patients.
Table 6.
A model for predicting grade II patient prognosis constructed with URGs.
Table 7.
A model for predicting grade III and IV patient prognosis constructed with URGs.
Fig 8.
Relationship between risk groups and clinical traits.
Analysis of the relationship between high and low risk groups and common clinical traits, including ATRX status, Chr 19/20 co-gain, Chr 7 gain/Chr 10 loss, MGMT promoter status, 1P/19q co-deletion, IDH status, mutation count, histology, grade, gender, age, and fustat. Gender and risk groups showed no correlation. TRIM38, CDC20, HOXB4 and other genes are the genes for constructing risk models. Red represents high expression, green represents low expression, and white represents intermediate expression. **p<0.01, ***p<0.001.
Fig 9.
Enriched signaling pathways and TFs related to risk model genes.
(A) GSEA enrichment analysis found that high-risk groups were enriched in ECM-receptor interaction, focal adhesion, homologous recombination, Jak-STAT signaling pathway, leukocyte transendothelial migration, mismatch repair, and the Toll-like receptor signaling pathway. (B) TFs that regulate risk model genes. Yellow triangles indicate TFs. Red ellipses and green quadrilaterals represent URGs; red ellipses indicate high-risk genes, and green quadrilaterals represent low-risk genes. The red line indicates a positive regulatory relationship, and the green line indicates a negative regulatory relationship.
Fig 10.
Relationship between risk score and immune cells.
The risk score was related to B cells (A), CD4 T cells (B), and neutrophils (F). However, it showed no statistical correlation with CD8 T cells (C), dendritic cells (D) and macrophages (E).