Backgrounds Decreased cytotoxicity of natural killer (NK) cells has been shown in multiple myeloma (MM). However, the underlying molecular mechanisms remain unclear. Here, by using single‐cell RNA sequencing analysis and in vitro experiments, we aim to uncover and validate molecularly distinctive insights into identifying regulators for NK cell exhaustion and provide potential targets for novel immune therapies in MM. Methods Single‐cell RNA sequencing was conducted in the bone marrow and peripheral blood samples from 10 newly diagnosed MM patients and three healthy volunteers. Based on the cluster‐defining differentially expressed genes, we named and estimated functional states of each cluster via bioinformatics analyses. Functional significance of key findings obtained from sequencing analysis was examined in a series of in vitro experiments, including luciferase reporter assay, lentiviral expression vector construction, NK cell transfection, RT‐qPCR, flow cytometry, and cytotoxicity assay. Results We classified NK cells into seven distinct clusters and confirmed that a subset of ZNF683 + NK cells were enriched in MM patients with ‘exhausted’ transcriptomic profile, featuring as decreased expression of activating receptors and cytolytic molecules, as well as increased expression of inhibitory receptors. Next, we found a significant downregulation of SH2D1B gene that encodes EAT‐2, an adaptor protein of activating receptor SLAMF7, in ZNF683 + NK cells from MM patients versus healthy volunteers. We further proved that ZNF683 transfection in NK cells significantly downregulated SH2D1B expression via directly binding to the promoter of SH2D1B , leading to NK cell cytotoxic activity impairment and exhausted phenotypes acquisition. In contrast, ZNF683 knockout in NK cells from MM patients increased cytotoxic activity and reversed NK cell exhaustion. Conclusions In summary, our findings uncover an important mechanism of ZNF683 + NK cell exhaustion and suggest that transcriptional suppressor ZNF683 as a potential useful therapeutic target in immunotherapy of MM.

Multiple myeloma (MM) is an incurable plasma cell malignancy with the hallmark of immunodeficiency, including dysfunction of T cells, NK cells, and APCs. Dysfunctional APCs have been reported to play a key role in promoting MM progression. However, the molecular mechanisms remain elusive. Here, single-cell transcriptome analysis of dendritic cells (DC) and monocytes from 10 MM patients and three healthy volunteers was performed. Both DCs and monocytes were divided into five distinct clusters, respectively. Among them, monocyte-derived DCs (mono-DC) were shown to develop from intermediate monocytes (IM) via trajectory analysis. Functional analysis showed that, compared with healthy controls, conventional DC2 (cDC2), mono-DC, and IM of MM patients exhibited impaired antigen processing and presentation capacity.Moreover, reduced regulon activity of interferon regulatory factor 1 (IRF1) was found in cDC2, mono-DC and IM of MM patients according to single-cell regulatory network inference and clustering (SCENIC) analysis, while the downstream mechanisms were distinct. Specifically in MM patients, cathepsin S (CTSS) was markedly downregulated in cDC2, major histocompatibility complex (MHC) class II transactivator (CIITA) was significantly decreased in IM, in addition both CTSS and CIITA were downregulated in mono-DC based on differentially expressed genes analysis. In vitro study validated that knockdown of Irf1 downregulated Ctss and Ciita respectively in mouse DC cell line DC2.4 and mouse monocyte/macrophage cell line RAW264.7, which ultimately inhibited proliferation of CD4 + T cells after being cocultured with DC2.4 or RAW264.7 cells. This current study unveils the distinct mechanisms of cDC2, IM, and mono-DC function impairment in MM, offering new insight into the pathogenesis of immunodeficiency.

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.