B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights From Bioinformatics, Machine...

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. GT-related genes were extracted from the MMRF-CoMMpass and GSE57317 data sets. Potential biomarkers were identified using Cox regression and Lasso analyses. A glycosyltransferase-related prognostic model (GTPM) was developed by evaluating 113 machine learning algorithm combinations. The expression of B4GALT3, a key gene identified through this model, was analyzed in MM bone marrow samples using immunohistochemistry, quantitative PCR, and Western blot. Functional roles of B4GALT3 in MM cell behavior were assessed through knockdown experiments, and its mechanism of action was investigated. The GTPM stratified MM patients into high- and low-risk groups, with significantly better survival in the low-risk group (HR = 55.94, 95% CI = 40.48-77.31, p < 0.001). The model achieved AUC values of 0.98 and 0.99 for 1- and 3-year overall survival, outperforming existing gene signatures (including EMC92, UAMS70, and UAMS17). B4GALT3 expression was significantly elevated in advanced MM stages (p < 0.001) and correlated with poorer survival. Knockdown of B4GALT3 reduced MM cell proliferation, invasion, and increased apoptosis. Mechanistic analyses revealed that B4GALT3 modulates MM cell behavior via the Wnt/β-catenin/GRP78 pathway, primarily by regulating endoplasmic reticulum (ER) stress. This study developed a novel GTPM for predicting survival in MM and identified B4GALT3 as a key gene influencing disease progression. Experimental evidence highlights B4GALT3's role in modulating ER stress and Wnt/β-catenin pathways, positioning it as a potential prognostic biomarker and therapeutic target in MM.