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"> A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature - Figure f1 | Aging

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Research Paper Volume 14, Issue 24 pp 9951—9968

A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature

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Figure 1. Selection of robust biomarkers to establish a prognostic UPS gene signature. (A) The 97 intersections of the OS related genes in MMRF-COMMPASS and GSE2658. (B) The LASSO coefficient profiles of the candidate OS-related UPS genes with nonzero coefficients. (C) A dotted vertical line represents the optimal value of the parameter (lambda) used in the LASSO model. (D) Multivariate Cox regression was used to establish the UPS gene signature, and six genes were finally selected as predictors of OS. (E) The mRNA levels of the 6 identified genes in training set (MMRF-COMMPASS). (F) Coefficient distribution of the gene signature.