Bioinformatics analysis of intrinsic drivers of immune dysregulation in multiple myeloma to elucidate immune phenotypes and discover prognostic gene signatures.

Multiple myeloma (MM) progression is driven by immune dysregulation within the tumor microenvironment (TME). However, myeloma-intrinsic mechanisms underlying immune dysfunction remain poorly defined, and current immunotherapies show limited efficacy. Using RNA-seq data from 859 MM patients (MMRF-CoMMpass), we integrated xCELL, CIBERSORT, and ESTIMATE algorithms to deconvolute immune-stromal dynamics. Consensus clustering identified immune subtypes, followed by differential gene analysis and LASSO-Cox regression to construct a prognostic model validated in an independent cohort (GSE19784, N = 328). Immune Subtype Classification: Two subgroups emerged: Multiple myeloma-associated immune-related cluster 1 (N = 482): Immune-dysfunctional TME with Th2 cell enrichment, preadipocyte accumulation, and CXCL family suppression, linked to poor survival (P < 0.001). Multiple myeloma-associated immune-related cluster 2 (N = 377): Immune-active TME with cytotoxic CD8 + T/NK cell infiltration and favorable outcomes. Prognostic Gene Signature: Ten immune-related genes (UBE2T, E2F2, EXO1, SH2D2A, DRP2, WNT9A, SHROOM3, TMC8, CDCA7, and GPR132) predicted survival (The One-year AUC = 0.682 and The Over 5-years AUC = 0.714). We define a myeloma-intrinsic immune classification system and a 10-gene prognostic index, offering a framework for risk-stratified immunotherapy. Integration with flow cytometry could optimize precision treatment in MM.