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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1491508
This article is part of the Research Topic Application of Bioinformatics, Machine Learning, and Artificial Intelligence to Improve Diagnosis, Prognosis and Treatment of Cancer View all 9 articles

Machine Learning Based Anoikis Signature Predicts Personalized Treatment Strategy of Breast Cancer

Provisionally accepted
Xiao Guo Xiao Guo 1Jiaying Xing Jiaying Xing 1Yuyan Cao Yuyan Cao 1Wenchuang Yang Wenchuang Yang 1Xinlin Shi Xinlin Shi 1Tao Wang Tao Wang 2*
  • 1 Beihua University, Jilin, Jilin Province, China
  • 2 Guizhou Provincial People's Hospital, Guiyang, China

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

    Breast cancer continues to be a major cause of mortality among women worldwide, underscoring the need for innovative prognostic methods to enhance treatment strategies. This study introduces the Artificial Intelligence-Derived Anoikis Signature (AIDAS), a novel prognostic tool that utilizes machine learning techniques to identify key anoikis-related gene patterns in breast cancer. The results show that AIDAS outperforms existing prognostic models in accurately predicting breast cancer outcomes, thus providing a robust tool for personalized treatment. Validation through immunohistochemistry assays on clinical samples confirms the model's reliability and broad applicability. Moreover, the analysis reveals that patients with low-AIDAS levels are more responsive to immunotherapy, while those with high-AIDAS levels are more susceptible to certain chemotherapeutic agents like methotrexate. This study highlights the importance of anoikis in breast cancer prognosis and presents AIDAS as an effective machine learning-based approach for personalized treatment. By offering a detailed understanding of the anoikis landscape in breast cancer, AIDAS paves the way for the development of targeted therapies, promising significant improvements in patient outcomes.

    Keywords: breast cancer, Anoikis, personalized treatment, PD-L1, Methotrexate

    Received: 05 Sep 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Guo, Xing, Cao, Yang, Shi and Wang. 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: Tao Wang, Guizhou Provincial People's Hospital, Guiyang, 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.