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

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.

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization

“… 18 , 19 High‐throughput microarray technology is widely used for the molecular diagnosis, classification and prognosis of MM, providing a novel avenue for the identification of potential biomarkers and pathways. 20 , 21 , 22 , 23 , 24 , 25 Despite the significant roles of certain biomarkers in the onset and progression of the disease during subsequent validation, challenges persist in establishing causality owing to issues such as reverse causation and confounding factors. Mendelian randomization (MR), an analytical approach aligned with Mendel's law of inheritance, utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to infer causality between a modifiable exposure and a clinically relevant outcome, which could address these limitations.…”

“…In the era of big biological data, the analysis of vast datasets is feasible by integrating biology, computer science and information technology 18,19 . High‐throughput microarray technology is widely used for the molecular diagnosis, classification and prognosis of MM, providing a novel avenue for the identification of potential biomarkers and pathways 20‐25 . Despite the significant roles of certain biomarkers in the onset and progression of the disease during subsequent validation, challenges persist in establishing causality owing to issues such as reverse causation and confounding factors.…”