ArticleDiscover oncology2025
The integrated single-cell analysis interpret the lactate metabolism-driven immune suppression in triple-negative breast cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Benmelstobart+anlotinib: an emerging therapeutic option in the targeted-immunotherapy era.Frontiers in oncology · 2026Review
- The cGAS-STING signaling pathway: emerging targets and challenges in breast cancer immunotherapy.Frontiers in immunology · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundIndividuals 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.
methodsTNBC 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.
resultsLactate-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.
conclusionThis 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.
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