Multi-omics reveals immune features in immune and non-immune cells, an IFN-γ/IFN-α-B2M positive feedback loop, and targeted metabolic therapy in multiple myeloma

Multiple myeloma (MM) is highly heterogeneous, with relapse occurring in the majority of cases, and recent advancements in single-cell RNA sequencing (scRNA-seq), sc-metabolism profiling, and bulk RNA-seq have facilitated the identification of cell subpopulations and metabolic reprogramming at the single-cell level, uncovering novel molecular mechanisms. This study aims to establish a multi-omics atlas of MM, characterizing the cell subpopulations and signaling pathways that drive immune evasion and disease progression. Additionally, sc-metabolic profiling identifies reprogramming patterns and informs therapeutic screening. We integrated scRNA-seq and bulk RNA-seq data using R to analyze immune and non-immune cell features and pathways in MM. Metabolic reprogramming was assessed via sc-metabolic profiling, and drug candidates were screened through multi-omics integration, with efficacy evaluated in vitro using CCK-8 assays, flow cytometry, Western blotting, and CalcuSyn software. Novel MM subpopulations were identified, including myeloma-activated hematopoietic stem cells and ISG15+ B cells, which correlated with survival and were validated by multiplex immunofluorescence. IFN-γ is primarily secreted by effector memory CD8+T cells, and IFN-α is primarily secreted by non-classical monocytes, driving an IFN-γ/α-B2M feedback loop. Multi-omics identified four drug candidates, each demonstrating anti-tumor effects against myeloma cell lines.