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
- 1 Department of Hematology, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
- 2 Department of Emergency, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
- 3 Department of Anesthesiology, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
- 4 Department of Endocrinology and Metabolism, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
- 5 Department of Oncology, First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
Received: October 8, 2022 Accepted: November 21, 2022 Published: December 18, 2022
https://doi.org/10.18632/aging.204432How to Cite
Copyright: © 2022 Ji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers.
Methods: RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used.
Results: A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (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.
Abbreviations
MM: Multiple myeloma; UPS: ubiquitin-proteasome system; MMRF: Multiple Myeloma Research Foundation; GEO: Gene Expression Omnibus; OS: overall survival; ROC: receiver operating characteristic; GSVA: Gene Set Variation Analysis.