Journal: NPJ Precision Oncology
Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature
doi: 10.1038/s41698-024-00692-w
Figure Lengend Snippet: A Time-dependent ROC curves displaying the prognostic accuracy of the risk score at 1, 3, and 5 years in TCGA, CGGA-693, CGGA-301, CGGA-325, GSE16011, and Rembrandt cohorts. B C-index of the risk score in the six cohorts. The performance of risk score was compared with other clinical and molecular variables in predicting prognosis in TCGA ( C ), CGGA-693 ( D ), GSE16011 ( E ), and Rembrandt ( F ) datasets. The performance of risk score + grade was compared with risk score and grade alone in predicting prognosis in TCGA ( G ), CGGA-693 ( H ), GSE16011 ( I ), and Rembrandt ( J ) datasets. K The 1-year, 2-year, and 3-year calibration curves of the CD163 + FPR3+ macrophage-associated signature in the four datasets. The P values are labeled above each boxplot with asterisks (-, no significant, “*, **, ***” respectively means P < 0.05, P < 0.01, P < 0.001).
Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).
Techniques: Labeling