Identification of Macrophage Polarization-Related Biomarkers in Multiple Myeloma Using Integrated Bioinformatics.

Abstract

Multiple myeloma (MM) is an incurable hematologic neoplasm with heterogeneous prognosis. Within the bone marrow microenvironment, macrophage polarization (MP) contributes to immune remodeling and may influence disease progression, yet MP-related prognostic biomarkers remain insufficiently characterized. In this study, we integrated transcriptomic data from UCSC Xena (n = 844) and GSE4581 (n = 414) to identify differentially expressed genes between immune-hot and immune-cold MM samples stratified by single-sample gene set enrichment analysis (ssGSEA)-derived immune scores. Weighted gene co-expression network analysis (WGCNA) and intersection with published MP-related genes yielded candidate genes, which were further refined using univariate Cox regression combined with least absolute shrinkage and selection operator (LASSO), eXtreme gradient boosting (XGBoost) and support vector machine recursive feature elimination (SVM-RFE) machine learning algorithms to construct a prognostic model. An eight-gene signature (LGALS3, IL2RG, ADAM8, MERTK, ACSS1, ARSK, CBX6 and SOCS3) was established, demonstrating acceptable discriminatory ability for 1-, 2-, and 3-year overall survival (AUC 0.666-0.736 in training cohort). Risk groups differed significantly in immune infiltration patterns, predicted drug sensitivity, and survival outcomes. A predicted competing endogenous RNA (ceRNA) network centered on MERTK and SOCS3 was also constructed. These findings identify eight MP-related hub genes as candidate prognostic biomarkers and provide a risk model with acceptable predictive performance, offering insights into the immune-metabolic interplay in MM and suggesting potential avenues for personalized therapeutic strategies.