{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16"],"submitter":["Min S"],"pubmed_abstract":["

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 drug-repurposing 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.

Conclusion

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."],"journal":["Frontiers in immunology"],"pagination":["1540018"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12095166"],"repository":["biostudies-literature"],"pubmed_title":["Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis."],"pmcid":["PMC12095166"],"pubmed_authors":["Liu Y","Sun M","Guan J","Zhang X","Wang T","Wang W","Chen Y","Min S","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis.","description":"

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 drug-repurposing 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.

Conclusion

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.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-03T02:42:56.788Z","creation":"2026-04-23T03:11:38.401Z"},"accession":"S-EPMC12095166","cross_references":{"pubmed":["40406140"],"doi":["10.3389/fimmu.2025.1540018"]}}