Skip to main content

ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1462953
This article is part of the Research Topic Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume II View all 6 articles

Evaluating the Prognostic Potential of Telomerase Signature in Breast Cancer Through Advanced Machine Learning Model

Provisionally accepted
Xiao Guo Xiao Guo 1Yuyan Cao Yuyan Cao 1Xinlin Shi Xinlin Shi 1Jiaying Xing Jiaying Xing 1Chuanbo Feng Chuanbo Feng 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 prognosis remains a critical challenge due to its molecular complexity. In this study, we developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating data from nine independent datasets. Using multiple machine learning models, we identified six key telomerase-related genes that significantly correlate with patient survival. The MLTS demonstrated high predictive accuracy, outperforming 66 existing breast cancer prediction models in terms of stability and reliability across diverse cohorts. Our findings revealed that high MLTS scores were associated with increased tumor mutational burden, chromosomal instability, and poor patient outcomes. Moreover, single-cell analysis highlighted the potential role of MLTS in driving cancer progression through elevated scores in aneuploid tumor cells. These results suggest that MLTS can serve as a valuable prognostic tool and a target for personalized therapy, paving the way for more precise and effective breast cancer management.

    Keywords: breast cancer, Telomerase genes, machine learning, PD-1, gemcitabine

    Received: 10 Jul 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Guo, Cao, Shi, Xing, Feng 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.