ArticleIUBMB life2023
N6-methyladenosine regulators are potential prognostic biomarkers for multiple myeloma.
Article in IUBMB life, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Emerging roles of RNA mInternational journal of oncology · 2026Review
- Prognostic value of fatty acid metabolism-related signature and integrated analysis of the immune microenvironment in multiple myeloma.BMC cancer · 2025Article
- Development of a prognostic model based on m6A readerTranslational cancer research · 2025Article
- Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.Briefings in bioinformatics · 2025Review
- Research progress on N6-methyladenosine and non-coding RNA in multiple myeloma.Discover oncology · 2025Review
- N6-methyladenosine-induced miR-182-5p promotes multiple myeloma tumorigenesis by regulating CAMK2N1.Molecular and cellular biochemistry · 2024Article
- RUVBL1/2 Blockade Targets YTHDF1 Activity to Suppress m6A-Dependent Oncogenic Translation and Colorectal Tumorigenesis.Cancer research · 2024Article
- KIAA1429 increases FOXM1 expression through YTHDF1-mediated m6A modification to promote aerobic glycolysis and tumorigenesis in multiple myeloma.Cell biology and toxicology · 2024Article
- Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma.Frontiers in oncology · 2023Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
N6-methyladenosine (m6A) regulators play an important role in tumorigenesis; however, their role in multiple myeloma (MM) remains unknown. This study aimed to create an m6A RNA regulators prognostic signature for MM patients. We integrated data from the Multiple Myeloma Research Foundation CoMMpass Study and the Genotype-Tissue Expression database to analyze gene expression profiles of 21 m6A regulators. Consistent clustering analysis was used to identify the clusters of patients with MM having different clinical outcomes. Gene distribution was analyzed using principal component analysis. Next, we generated an mRNA gene signature of m6A regulators using a multivariate logistic regression model with least absolute shrinkage and selection operator. The expressions of m6A regulators, except FMR1, were significantly different in MM samples compared with those in normal samples. The KIAA1429, HNRNPC, FTO, and WTAP expression levels were dramatically downregulated in tumor samples, whereas those of other signatures were remarkably upregulated. Three clusters of patients with MM were identified, and significant differences were found in terms of overall survival (p = .024). A prognostic two-gene signature (KIAA1429 and HNRNPA2B1) was constructed, which had a good prognostic significance using the ROC method (AUC = 0.792). Moreover, the risk score correlated with the infiltration immune cells. In addition, KEGG pathway analysis showed that 16 pathways were dramatically enriched. The m6A signature might be a novel biomarker for predicting the prognosis of patients with MM (p = .002). Our study is the first to explore the potential application value of m6A in MM. These findings may enhance the understanding of the functional organization of m6A in MM and provide new insights into the treatment of MM patients.
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