Identification of Metabolomics Markers of Breast Cancer using Machine Learning approach.
Year: 2025
Type: article
Abstract: : Breast cancer remains a leading cause of cancer-related mortality in women worldwide, necessitating the identification of reliable biomarkers for early detection, prognosis, and therapeutic response. Metabolomics, the comprehensive study of small-molecule metabolites, offers a promising avenue for biomarker discovery by capturing biochemical alterations associated with breast cancer progression. In this study, we employ a machine learning-based approach to analyze metabolomic profiles from breast cancer patients and healthy controls, aiming to identify key metabolic signatures associated with disease status. High-resolution mass spectrometry-based metabolomics data, previously published, was acquired from tissue samples of breast cancer patients and their matched controls are used in present study. Several supervised machines learning models, including Random Forest, Logistic Regression and Neural Network were employed to classify the metabolites between tumor and matched benign tissue samples. AUROC scores and pathway enrichment analyses were used to interpret the biological significance of the identified metabolites. Our results demonstrate that machine learning models achieved high classification accuracy, with Random Forest outperforming other methods. Several metabolites, including xenobiotic metabolism, central carbon metabolites, amino acid derivatives, were identified as significant contributors to breast cancer classification. Pathway analysis revealed key Hallmark pathways, highlighting potential metabolic vulnerabilities in breast cancer. This study highlights the potential of integrating metabolomics with machine learning to uncover novel metabolic biomarkers for breast cancer. The identified metabolomic signatures may serve as valuable tools for early detection and personalized therapeutic strategies. Future studies will focus on validating these biomarkers in larger, independent cohorts and exploring their mechanistic roles in breast cancer pathophysiology. Keywords: Breast cancer, Metabolomics and Machine learning.
Author: Vasanta Putluri
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6911914717
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