约有10%的血液恶性肿瘤是多发性骨髓瘤（MM），这是一种无法治疗的癌症。尽管乳酸和支链氨基酸（BCAA）参与支持各种肿瘤的生长，但目前尚不清楚它们是否与MM的预后有关。我们从基因表达公共数据库（GEO）和癌症基因组图谱数据库（TCGA）中获取了与MM相关的数据集（GSE4581、GSE136337和TCGA-MM）。我们使用R包“ConsensusClusterPlus”在GSE4281数据集中分别获得了乳酸和BCAA代谢相关的亚型。我们使用R包“limma”和Venn图表鉴定了与乳酸-BCAA代谢相关的基因。随后，我们通过单变量Cox回归分析、最小绝对收缩和选择算子（LASSO）以及多变量Cox回归分析构建了一种与MM患者乳酸-BCAA代谢相关的预后风险模型。我们采用基因集富集分析（GSEA）和R包“clusterProfiler”进行了两组之间生物学变化的探索。此外，我们还使用单样本基因集富集分析（ssGSEA）、微环境细胞种群计数（MCPcounter）和xCell技术评估了MM中的肿瘤微环境（TME）评分。最后，我们使用“oncoPredict”包计算了治疗MM的药物IC50，并通过分子对接进一步进行了药物鉴定。在乳酸代谢相关亚型和BCAA代谢相关亚型中，群集1表现出比群集2更差的预后。确定了244个基因参与了MM的乳酸-BCAA代谢。由此选择的CKS2和LYZ构建了MM的预后风险模型，并且该预后风险模型在外部数据集中也是稳定的。对高风险组而言，共有13个条目进行了富集。低风险组富集了16个条目。免疫评分、基质评分、免疫浸润细胞（除ssGSEA算法中的17型T辅助细胞外）和168种药物的IC50在两组之间具有统计学差异。烷基化剂可能作为MM治疗的新药物。CKS2和LYZ被确定为MM的乳酸-BCAA代谢相关基因，然后通过使用它们构建了一种新的预后风险模型。综上所述，本研究可能揭示了MM治疗和预后的新特征基因特征签名。 © 2023. BioMed Central Ltd., Springer Nature出版集团的一部分。

About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis.MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package "ConsensusClusterPlus" in the GSE4281 dataset. The R package "limma" and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package "clusterProfiler"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug's IC50 for treating MM was calculated using the "oncoPredict" package, and further drug identification was performed by molecular docking.Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs'IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment.CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM.© 2023. BioMed Central Ltd., part of Springer Nature.