AUTHOR=Min Simin , Zhang Xiaonan , Liu Yuling , Wang Weiqiang , Guan Jingwen , Chen Yuyan , Sun Meng , Wang Ziheng , Wang Tao 

TITLE=Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1540018

DOI=10.3389/fimmu.2025.1540018

ISSN=1664-3224

ABSTRACT=BackgroundBreast cancer is a heterogeneous malignancy with complex molecular characteristics, making accurate prognostication and treatment stratification particularly challenging. Emerging evidence suggests that lactylation, a novel post-translational modification, plays a crucial role in tumor progression and immune modulation.MethodsTo address breast cancer heterogeneity, we developed a machine learning-derived lactylation signature (MLLS) using lactylation-related genes selected through random survival forest (RSF) and univariate Cox regression analyses. A total of 108 algorithmic combinations were applied across multiple datasets to construct and validate the model. Immune microenvironment characteristics were analyzed using multiple immune infiltration algorithms. Computational drug-repurposing analyses were conducted to identify potential therapeutic agents for high-risk patients.ResultsThe MLLS effectively stratified patients into low- and high-risk groups with significantly different prognoses. The model demonstrated robust predictive power across multiple cohorts. Immune infiltration analysis revealed that the low-risk group exhibited higher levels of immune checkpoints (e.g., PD-1, PD-L1) and greater infiltration of B cells, CD4+ T cells, and CD8+ T cells, suggesting better responsiveness to immunotherapy. In contrast, the high-risk group showed immune suppression features associated with poor prognosis. Methotrexate was computationally predicted as a potential therapeutic candidate for high-risk patients, although experimental validation remains necessary.ConclusionThe MLLS represents a promising prognostic biomarker and may support personalized treatment strategies in breast cancer, particularly for identifying candidates who may benefit from immunotherapy.