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CC BY 4.0 · Brazilian Journal of Oncology 2024; 20
DOI: 10.1055/s-0044-1791655
Original Article
Clinical Oncology

Devising a Breast Cancer Diagnosis Protocol through Machine Learning

Tooba Mujtaba 
1   Department of Bioinformatics, COMSATS University Islamabad, Islamabad, Pakistan.
,
Saif Ullah Hashmi 
1   Department of Bioinformatics, COMSATS University Islamabad, Islamabad, Pakistan.
,
Usama Bin Imtiaz 
1   Department of Bioinformatics, COMSATS University Islamabad, Islamabad, Pakistan.
,
Sheikh Jameel Fathima Nusra 
1   Department of Bioinformatics, COMSATS University Islamabad, Islamabad, Pakistan.
› Author Affiliations Funding The authors declare that they did not receive funding from agencies in the public, private or non-profit sectors to conduct the present study.
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Abstract

Breast cancer is a life-threatening disease and has serious health implications. It is categorized based on receptors, including the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), which are the focus of the present research We analyzed gene expression from data obtained from a functional genomics repository called Array Express. The accession numbers are E-GEOD-52194, E-GEOD-75367, and E-GEOD-58135, and the molecular details of these subsets of cancer receptors. Upon following a predefined computational pipeline, we identified 369 genes that had distinct patterns of gene expression profiles in cases of ER-positive (ER + ) and HER2-negative (HER2-) breast cancer. The support vector machine (SVM) and decision tree models of machine learning were used to evaluate the prognostic and diagnostic significance. Accuracy, sensitivity, and specificity were examined to gauge the effectiveness of these models. Then, a network analysis was performed to assess the significant biological process and signaling pathways of HER2- and ER+ breast cancer development. The present study facilitates an enhanced approach to these subcategories of breast cancer so that precise diagnoses can be made, and better and more focused treatment plans can be provided. The current research provides valuable information on the molecular and genetic basis of ER+ and HER2- breast cancer and has great potential for improving patients' treatment.

Keywords

machine learning - supervised machine learning - decision trees

Author's Contributions

TM: collection and assembly of data, conception and design, data analysis and interpretation, final approval of manuscript, manuscript writing, and provision of study materials or patients. SUH: collection and assembly of data, conception, and design. UBI: data analysis and interpretation, and final approval of manuscript. and SJFN: provision of study materials or patients.




Publication History

Received: 23 March 2024

Accepted: 22 August 2024

Article published online:
18 October 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

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Bibliographical Record
Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra. Devising a Breast Cancer Diagnosis Protocol through Machine Learning. Brazilian Journal of Oncology 2024; 20.
DOI: 10.1055/s-0044-1791655