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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1521930
This article is part of the Research Topic Big Data and Precision Medicine: Diagnosis and Treatment, Drug Discovery, and Integration of Multiple Omics View all 11 articles

Glycosylation Profiling of Triple-Negative Breast Cancer: Clinical and Immune Correlations and Identification of LMAN1L as a Biomarker and Therapeutic Target

Provisionally accepted
Qianru Yu Qianru Yu 1Hanyi Zhong Hanyi Zhong 2Xinhao Zhu Xinhao Zhu 3Chang Liu Chang Liu 1Xin Zhang Xin Zhang 1Jiao Wang Jiao Wang 1Zongyao Li Zongyao Li 3Songchang Shi Songchang Shi 3Haoran Zhao Haoran Zhao 3Cixiang Zhou Cixiang Zhou 1*Qian Zhao Qian Zhao 1*
  • 1 Key Laboratory of Cell Differentiation and Apoptosis, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
  • 2 Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, Beijing, China
  • 3 School of Medicine, Shanghai Jiao Tong University, Shanghai, China

The final, formatted version of the article will be published soon.

    Breast cancer (BC) is the most prevalent malignant tumor in women, with triplenegative 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. In this study, we identified a set of glycosylation-related genes associated with TNBC prognosis. Using these genes, we developed a machine learning-based prediction model, which was validated using multiple datasets. Our model can also predict key molecular features of tumors, such as immune cell infiltration levels and potential responses to immunotherapy in TNBC patients. We also identified LMAN1L (Lectin, Mannose Binding 1 Like) as a glycosylation-related gene that is newly associated with TNBC prognosis. Experimental validation showed that low expression of LMAN1L inhibits TNBC cell proliferation and migration. In conclusion, our glycosylation-related gene prognostic model demonstrated robust validation in TNBC. This model holds significant potential for predicting patient prognosis and immunotherapy response, offering new strategies for TNBC treatment.

    Keywords: TNBC, Glycosylation, machine learning, prognosis, Tumor immune microenvironment

    Received: 03 Nov 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 Yu, Zhong, Zhu, Liu, Zhang, Wang, Li, Shi, Zhao, Zhou and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Cixiang Zhou, Key Laboratory of Cell Differentiation and Apoptosis, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
    Qian Zhao, Key Laboratory of Cell Differentiation and Apoptosis, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.