ArticleAging2022
A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature.
Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Roles of the E3 Ubiquitin Ligase TRIM47 in Inflammation, Organ Injury, and Cancer.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights.Blood and lymphatic cancer : targets and therapy · 2025Article
- Unraveling the Role of Ubiquitin-Conjugating Enzyme UBE2T in Tumorigenesis: A Comprehensive Review.Cells · 2024Review
- Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.Journal of cellular and molecular medicine · 2024Article
- Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization.Journal of cellular and molecular medicine · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple 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.
methodsRNA 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.
resultsA 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.
conclusionsAs 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.
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