The significance of lactate and lactate metabolism in the development and progression of certain solid tumors has been reported. However, the role of lactate metabolism in the prognosis of multiple myeloma (MM) patients and in the tumor microenvironment (TME) remains unclear.

This study aimed to construct a prognostic risk-scoring model for MM patients based on lactate metabolism and to explore the impact of lactate metabolism on the TME.

Transcriptomic sequencing expression data and survival information of MM samples were obtained from the Gene Expression Omnibus (GEO) datasets GSE136324 and GSE4581. Lactate metabolism-related genes (LMRGs) for subsequent analysis were identified through the MSigDB and GeneCards databases. Consensus clustering and Cibersort were applied to explore the relationship between LMRGs and the TME. Differences in the TME among MM samples were analyzed using the Wilcoxon rank-sum test. A linear regression model was used to identify LMRGs associated with survival prognosis, and results were visualized using forest plots and Kaplan-Meier survival curves, with comparisons made by log-rank test. Least absolute shrinkage and selection operator (Lasso) regression was used for variable selection, and univariate and multivariate Cox regression analyses were performed to identify prognostic LMRGs for model construction. The predictive performance of the model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Pearson correlation analysis was used to examine the relationship between the lactate metabolism-based risk score and immune score, stromal score, and tumor purity.

Based on the expression profiles of LMRGs, consensus clustering via the ConsensusClusterPlus package identified two lactate metabolism-related subtypes: Lactate.cluster A and Lactate.cluster B. Patients in Lactate.cluster A had significantly worse prognosis than those in Lactate.cluster B (χ²=19.11, P<0.000 1) . Significant differences were observed between the two subtypes in the infiltration levels of naive B cells, memory B cells, plasma cells, CD₈⁺ T cells, resting memory CD₄⁺ T cells, activated memory CD₄⁺ T cells, follicular helper T cells, gamma delta T cells, resting natural killer (NK) cells, activated NK cells, monocytes, M0 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils (P<0.05) . Several LMRGs were significantly associated with prognosis: high expression of PC, POLG, FASN, FABP2, SLC7A5, BCAT2, SLC16A8, HAGH, PFKL, PGK2, PKLR, CA2, and SLC2A1 was associated with better prognosis, while high expression of PDP1, AGK, PEX12, and PFKFB2 was associated with poorer prognosis (P<0.05) . Eleven independent predictive variables—SLC2A1, CA2, PKLR, PFKL, PFKFB2, SLC16A8, SLC7A5, AGK, FABP2, POLG, and PC—were included in the multivariate Cox regression analysis to construct a predictive model. Based on the median risk score, patients were divided into high-risk and low-risk groups. In the training set, the high-risk group had significantly worse overall survival than the low-risk group (χ²=59.02, P<0.05) . The area under the ROC curve (AUC) for predicting 5-year survival was 0.781 (95%CI=0.664-0.886) . In the validation set, the high-risk group also had significantly poorer survival (χ²=9.24, P<0.05) , with an AUC of 0.64 (95%CI=0.542-0.737) for predicting 1-year survival. Analysis of immunotherapy response data from the training dataset showed a significant difference in outcomes between the high- and low-risk groups (Z=-2.469, P=0.014) , with high-risk score being an independent prognostic factor (P<0.05) . Comparisons of immune cell infiltration between the two groups revealed significant differences in plasma cells, CD₈⁺ T cells, resting memory CD₄⁺ T cells, activated memory CD₄⁺ T cells, follicular helper T cells, resting NK cells, activated NK cells, monocytes, M0 macrophages, resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, and eosinophils (P<0.05) . Significant differences were also observed in the expression of immune-related genes including HAVCR2, CD₂₇₄, PVR, CD₈₀, CTLA4, PDCD₁, CD₂₀₀R1, CD₂₇₆, CD₂₀₀, BTLA, LGALS3, and VTCN1 (P<0.05) . Pearson correlation analysis between the lactate metabolism-based risk score and enrichment scores of 50 hallmark pathways revealed positive correlations with WNT beta-catenin signaling, androgen response, and UV response, and negative correlations with KRAS signaling, pancreas beta cells, and heme metabolism (P<0.05) . The risk score was positively correlated with stromal score (P<0.05) but not with tumor purity (P>0.05) .

This study constructed a risk score prognostic model for multiple myeloma patients based on lactate metabolism and validated it with independent datasets. The new prognostic model is robust and demonstrates better performance in predicting long-term survival. Furthermore, the analysis of lactate metabolism in relation to the TME suggests that lactate metabolism may influence immune cell populations within the TME of MM, thereby affecting tumor progression and the prognosis of MM patients.