Article

B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation

2025-01-28

Abstract excerpt

<title>Abstract</title> <p>Background: Glycosylation abnormalities are critical in the progression of various cancers. However, their role in the onset and prognosis of multiple myeloma (MM) remains underexplored. This study aims to identify glycosyltransferase (GT)-related biomarkers and investigate their underlying mechanisms in MM. <h4>Methods:</h4> GT-related genes were extracted from the MMRF-CoMMpass and GS...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4f143189-e518-558b-8541-b8b4d7544af3
DOI: 10.21203/rs.3.rs-5882070/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental ValidationDOI 10.21203/rs.3.rs-5882070/v1Read

Exploring the Molecular Mechanisms and Shared Genetic Characteristics of Monoclonal Gammopathy of Undetermined Significance and Multiple MyelomaSemantic proximity Read

Gene Expression and Clinical Relevance Analysis of ALG3 as an Adverse Prognosis Biomaker in Multiple Myeloma Based on Oncomine Database and CCLE DatabaseSemantic proximity Read

Key Cancer Glycosylation-related Genes Analysis: Regulatory, Therapeutic, and Prognostic InsightsSemantic proximity Read

Predictive potential of cuproptosis-related genes in multiple myeloma: Comprehensive analysis based on bone marrow whole-genome sequencingSemantic proximity Read

Identification of a novel lactylation-related gene signature predicts the prognosis of multiple myeloma and experiment verificationSemantic proximity Read

Biological Hallmarks and Emerging Strategies to Target STAT3 Signaling in Multiple MyelomaSemantic proximity Read

Individualized dynamic risk assessment for multiple myelomaSemantic proximity Read

Machine Learning Models for Predicting Multiple Myeloma Staging and MGUS Progression Using Gene Expression DataSemantic proximity Read

Utilizing Genomics to Identify Novel Immunotherapeutic Targets in Multiple Myeloma High-Risk SubgroupsSemantic proximity Read

Multi-omics analysis of multiple myeloma patients with differential response to first-line treatment.Semantic proximity Read

Genome-Wide Identification of Potential Biomarkers in Multiple Myeloma Using Meta-Analysis of mRNA and miRNA Expression DataSemantic proximity Read

Genotype-immunophenotype analysis reveals the immunogenomic subtype and prognosis of multiple myeloma.Semantic proximity Read