Personalized Treatment Decision-Making Using a Machine Learning-Derived Lactylation Signature for Breast Cancer Prognosis

Simin Min¹Xiaonan Zhang²Yuling Liu¹Weiqiang Wang¹Jingwen Guan¹Yuyan Chen¹Meng Sun¹Ziheng Wang²Tao Wang^3*

• ¹Suzhou Hospital of Anhui Medical University, Suzhou, China
• ²Bengbu Medical Universiy, Bengbu, Anhui Province, China
• ³Guizhou Provincial People's Hospital, Guiyang, China

Background: Breast 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. Methods: To 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 drugrepurposing analyses were conducted to identify potential therapeutic agents for high-risk patients. Results: The 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.The MLLS represents a promising prognostic biomarker and may support personalized treatment strategies in breast cancer, particularly for identifying candidates who may benefit from immunotherapy.