https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets.
Conclusions: As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM." name="description">
Figure 5. Validation the reliability of the risk signature in the validation set (GSE2658). (A) summarizes the distribution of risk scores, the survival status of patients, and the expression of UPS genes in the training set. (B) A time-dependent ROC analysis for 1-, 3-, and 5-years OS prediction. (C) Survival analysis between low-risk and high-risk MM patients.