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Abstract：
BACKGROUND: Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers.
METHODS: Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.
RESULTS: A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.
CONCLUSIONS: A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.
METHODS: Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.
RESULTS: A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.
CONCLUSIONS: A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.
摘要：
背景：自然杀伤细胞（NK）可能参与多发性骨髓瘤（MM）的进展。本研究使用单细胞结合转录组探针鉴定NK细胞相关生物标志物阐明了NK与MM进展之间的相关性。
方法：进行单细胞分析，包括细胞和亚型注释，细胞通讯，和伪时间分析。使用基因本体论对NK和NK相关差异表达基因（DEGs）进行了Hallmark途径富集分析，京都基因和基因组百科全书，和蛋白质-蛋白质相互作用(PPI)网络。然后，基于通过单变量Cox回归分析和最小绝对收缩率和选择算子回归分析确定的生物标志物构建风险模型,并随后进行验证.此外,临床特征的相关性，基因集富集分析，免疫分析,监管网络,并对药物预测进行了探索。
结果：共获得13个细胞簇并注释，包括由NK组成的8个细胞群。利用123个PPI网络节点基因，选择8个NK相关的DEGs构建预后模型。免疫细胞浸润结果表明，11个免疫细胞在高危和低危组中表现出明显差异。最后,该模型用于筛选潜在的药物靶点以增强免疫治疗效果.
结论：构建并验证了与NK相关的MM的新预后模型。该模型为预测患者预后提供了新的视角，免疫治疗反应，和候选药物。
方法：进行单细胞分析，包括细胞和亚型注释，细胞通讯，和伪时间分析。使用基因本体论对NK和NK相关差异表达基因（DEGs）进行了Hallmark途径富集分析，京都基因和基因组百科全书，和蛋白质-蛋白质相互作用(PPI)网络。然后，基于通过单变量Cox回归分析和最小绝对收缩率和选择算子回归分析确定的生物标志物构建风险模型,并随后进行验证.此外,临床特征的相关性，基因集富集分析，免疫分析,监管网络,并对药物预测进行了探索。
结果：共获得13个细胞簇并注释，包括由NK组成的8个细胞群。利用123个PPI网络节点基因，选择8个NK相关的DEGs构建预后模型。免疫细胞浸润结果表明，11个免疫细胞在高危和低危组中表现出明显差异。最后,该模型用于筛选潜在的药物靶点以增强免疫治疗效果.
结论：构建并验证了与NK相关的MM的新预后模型。该模型为预测患者预后提供了新的视角，免疫治疗反应，和候选药物。
