Version 1
: Received: 14 May 2024 / Approved: 15 May 2024 / Online: 15 May 2024 (12:29:53 CEST)
How to cite:
Vaida, M. L.; Arumalla, K.; Tatikonda, P.; Popuri, B.; Bux, R.; Tappia, P. S.; Huang, G.; Haince, J.-F.; Ford, W. R. Identifying Robust Biomarker Panels for Breast Cancer Screening. Preprints2024, 2024050996. https://doi.org/10.20944/preprints202405.0996.v1
Vaida, M. L.; Arumalla, K.; Tatikonda, P.; Popuri, B.; Bux, R.; Tappia, P. S.; Huang, G.; Haince, J.-F.; Ford, W. R. Identifying Robust Biomarker Panels for Breast Cancer Screening. Preprints 2024, 2024050996. https://doi.org/10.20944/preprints202405.0996.v1
Vaida, M. L.; Arumalla, K.; Tatikonda, P.; Popuri, B.; Bux, R.; Tappia, P. S.; Huang, G.; Haince, J.-F.; Ford, W. R. Identifying Robust Biomarker Panels for Breast Cancer Screening. Preprints2024, 2024050996. https://doi.org/10.20944/preprints202405.0996.v1
APA Style
Vaida, M. L., Arumalla, K., Tatikonda, P., Popuri, B., Bux, R., Tappia, P. S., Huang, G., Haince, J. F., & Ford, W. R. (2024). Identifying Robust Biomarker Panels for Breast Cancer Screening. Preprints. https://doi.org/10.20944/preprints202405.0996.v1
Chicago/Turabian Style
Vaida, M. L., Jean-François Haince and W. Randolph Ford. 2024 "Identifying Robust Biomarker Panels for Breast Cancer Screening" Preprints. https://doi.org/10.20944/preprints202405.0996.v1
Abstract
Breast cancer remains a major public health concern, and early detection can result in more treatment options, which are crucial for improving survival rates. Metabolomics offers the potential to develop blood-based screening and diagnostics tools that are less invasive and more cost-effective. However, the inherent complexity of metabolomic datasets makes identifying the most diagnostically relevant biomarkers a difficult task, with multiple studies demonstrating lim-ited agreement on the specific metabolites and pathways involved. This study aims to identify a set of biomarkers for early diagnosis of breast cancer using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (CHTN) were analyzed. We utilized univariate Naïve Bayes, L2-regularized Support Vector Machines, and Principal Component Analysis (PCA), along with feature engineering techniques, to select the most informative features. Multiple ma-chine learning models, including Support Vector Machines, Multidimensional Scaling, Logistic Regression, and Ensemble Learning were utilized for classification. The best-performing feature set comprised 4 biomarkers and 2 demographic variables, achieving an accuracy of 98%, demon-strating the potential for a robust, cost-effective, non-invasive breast cancer screening and diagnostic tool.
Keywords
breast cancer; biomarkers; metabolomic profiling; early detection; screening
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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