The predictive value of metabolomic-related biomarkers in breast cancers: Current approaches in biotechnology

Ahishtan Febrian Nishanthan; Ermira Jahja; Tamar Peshkova; Nameera Parveen Shaikh; Aleena Parveen Shaikh; Joy Dip Barua; Irina Nakashidze

doi:10.62063/rev-22

Authors

Ahishtan Febrian Nishanthan Batumi Shota Rustaveli State University https://orcid.org/0009-0005-3415-4205
Ermira Jahja Western Balkans University https://orcid.org/0000-0003-2941-5173
Tamar Peshkova Batumi Shota Rustaveli State University https://orcid.org/0009-0009-4579-5170
Nameera Parveen Shaikh Toronto Metropolitan University https://orcid.org/0000-0002-7393-0263
Aleena Parveen Shaikh Batumi Shota Rustaveli State University https://orcid.org/0000-0002-1473-4334
Joy Dip Barua Pondicherry University https://orcid.org/0000-0002-0392-8213
Irina Nakashidze Batumi Shota Rustaveli State University

DOI:

https://doi.org/10.62063/rev-22

Keywords:

AI, Biotechnology, Lipids, Metabolomics, Breast Cancer

Abstract

Breast cancer is the second most common cancer worldwide and is a leading cause of cancer-related mortality in women. The rising burden of breast cancer highlights the need for more accurate, non-invasive, and informative diagnostic tools. Despite the current advancements in medicine, predicting treatment response and patient prognosis remains challenging. It has thus become imperative to address the need for precise and reliable prognostic and diagnostic tools. Metabolic profiles, such as lipid processing and steroid hormone metabolism, have recently emerged as significant biomarkers in tumor biology, especially for early detection, prognosis, and therapy monitoring. This literature review explores the predictive value of serum lipid profiles and selected steroids as biomarkers in breast tumors. It shows their potential in improving diagnostic strategies and treatment planning in breast cancer management. These approaches offer valuable insights into tumor biology, metabolic changes, and hormone-driven pathways. Despite current challenges in sample preparation, data interpretation, and technical demands, recent advances such as high-resolution mass spectrometry, as well as spatial metabolomics and artificial intelligence, are helping to overcome these barriers. With continued research and technological progress, metabolomic-related biomarkers are expected to see broader use in clinical settings, supporting personalized treatment and improving outcomes for breast cancer patients.

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