Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target

• Published in: Frontiers in immunology 2025-01, Vol.15
• Main Authors: Yu, Qianru, Zhong, Hanyi, Zhu, Xinhao, Liu, Chang, Zhang, Xin, Wang, Jiao, Li, Zongyao, Shi, Songchang, Zhao, Haoran, Zhou, Cixiang, Zhao, Qian
• Format: Article
• Language: English
• Subjects: glycosylation; machine learning; prognosis; TNBC; tumor immune microenvironment
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2024.1521930

IntroductionBreast cancer (BC) is the most prevalent malignant tumor in women, with triple-negative breast cancer (TNBC) showing the poorest prognosis among all subtypes. Glycosylation is increasingly recognized as a critical biomarker in the tumor microenvironment, particularly in BC. However, the glycosylation-related genes associated with TNBC have not yet been defined. Additionally, their characteristics and relationship with prognosis have not been deeply investigated.MethodsTranscriptomic analyses were used to identify a glycosylation-related signature (GRS) associated with TNBC prognosis. A machine learning-based prediction model was constructed and validated across multiple independent datasets. The model's predictive capability was extended to evaluate the prognosis of TNBC individuals, tumor immune microenvironment and immunotherapy response. LMAN1L (Lectin, Mannose Binding 1 Like) was identified as a novel prognostic marker in TNBC, and its biological effects were validated through experimental assays.ResultsThe GRS showed significant prognostic relevance for TNBC patients. The risk model effectively predicted molecular features, including immune cell infiltration and potential responses to immunotherapy. Experimental validation confirmed LMAN1L as a novel glycosylation-related prognostic gene, with low expression significantly inhibiting TNBC cell proliferation and migration.DiscussionOur GRS risk model demonstrates robust predictive capability for TNBC prognosis and immunotherapy response. This model offers a promising strategy for personalized treatment and improved clinical outcomes in TNBC.