Skip Navigation
Skip to contents

Cancer Res Treat : Cancer Research and Treatment

OPEN ACCESS

Articles

Page Path
HOME > J Korean Cancer Assoc > Accepted articles > Article
Original Article
Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer
Ju Won Kim1orcid , Jonghyun Lee2orcid , Sung Hak Lee3, Sangjeong Ahn2orcid , Kyong Hwa Park1orcid

DOI: https://doi.org/10.4143/crt.2024.937 [Accepted]
Published online: November 19, 2024
1Division of Hemato-oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
2Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
3Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
Corresponding author:  Sangjeong Ahn
Tel: 82-2-920-5621 Fax: 82-2-2199-3918  Email: vanitas80@korea.ac.kr
Kyong Hwa Park
Tel: 82-2-920-6841 Fax: 82-2-2199-3918  Email: khpark@korea.ac.kr
Ju Won Kim and Jonghyun Lee contributed equally to this work.
Received: 26 September 2024   • Accepted: 18 November 2024
  • 284 Views
  • 19 Download
  • 0 Crossref
  • 0 Scopus

Purpose
This study aimed to develop a machine learning-based approach to identify prognostic gene signatures for early-stage Triple Negative Breast Cancer (TNBC) using next-generation sequencing data from Asian populations.
Materials and Methods
We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.
Results
We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve (AUROC) of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.
Conclusion
This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.

  • Cite
    CITE
    export Copy Download
    Close
    Download Citation
    Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

    Format:
    • RIS — For EndNote, ProCite, RefWorks, and most other reference management software
    • BibTeX — For JabRef, BibDesk, and other BibTeX-specific software
    Include:
    • Citation for the content below
    Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer
    Close
Related articles

Cancer Res Treat : Cancer Research and Treatment
Close layer
TOP