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Background <p>Individuals with triple-negative breast cancer (TNBC) exhibit elevated lactate levels, which offers a valuable lead for investigating the molecular mechanisms underlying the tumor microenvironment (TME) and identifying more efficacious treatments.</p> Methods <p>TNBC samples were classified based on lactate-associated genes. A single-cell transcriptomic approach was employed to examine functional differences across cells with varying lactate metabolism. Immunohistochemistry was used to explore the relationship between lactate metabolism and the CXCL12/CXCR4 signaling axis. In addition, utilizing machine learning techniques, we constructed a prognostic model based on lactic acid phenotype genes.</p> Results <p>Lactate-associated gene-based stratification revealed increased immune cell infiltration and immune checkpoint expression in Lactate Cluster 1. Elevated lactate metabolism scores were observed in both cancer-associated fibroblasts (CAFs) and malignant cells. CAFs with high lactate metabolism exhibited immune suppression through the CXCL12/CXCR4 axis. Immunohistochemistry confirmed elevated LDHA, LDHB, CXCL12, and CXCR4 levels in the high lactate group.</p> Conclusion <p>This study elucidates the complex interplay between lactate and immune cells in TNBC and highlights the CXCL12/CXCR4 axis as a key pathway through which lactate mediates immune suppression, offering new insights into metabolic regulation within the TME. Furthermore, we developed a prognostic model based on lactate metabolism phenotype genes to predict the prognosis of TNBC patients and guide immunotherapy.</p>

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The integrated single-cell analysis interpret the lactate metabolism-driven immune suppression in triple-negative breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月16日

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  • Xinhai Gao,
  • Tianhua Wang,
  • Cun Liu,
  • Ye Li,
  • Wenfeng Zhang,
  • Minpu Zhang,
  • Yan Yao,
  • Chundi Gao,
  • Ruijuan Liu,
  • Changgang Sun

摘要

Background

Individuals with triple-negative breast cancer (TNBC) exhibit elevated lactate levels, which offers a valuable lead for investigating the molecular mechanisms underlying the tumor microenvironment (TME) and identifying more efficacious treatments.

Methods

TNBC samples were classified based on lactate-associated genes. A single-cell transcriptomic approach was employed to examine functional differences across cells with varying lactate metabolism. Immunohistochemistry was used to explore the relationship between lactate metabolism and the CXCL12/CXCR4 signaling axis. In addition, utilizing machine learning techniques, we constructed a prognostic model based on lactic acid phenotype genes.

Results

Lactate-associated gene-based stratification revealed increased immune cell infiltration and immune checkpoint expression in Lactate Cluster 1. Elevated lactate metabolism scores were observed in both cancer-associated fibroblasts (CAFs) and malignant cells. CAFs with high lactate metabolism exhibited immune suppression through the CXCL12/CXCR4 axis. Immunohistochemistry confirmed elevated LDHA, LDHB, CXCL12, and CXCR4 levels in the high lactate group.

Conclusion

This study elucidates the complex interplay between lactate and immune cells in TNBC and highlights the CXCL12/CXCR4 axis as a key pathway through which lactate mediates immune suppression, offering new insights into metabolic regulation within the TME. Furthermore, we developed a prognostic model based on lactate metabolism phenotype genes to predict the prognosis of TNBC patients and guide immunotherapy.