Machine learning–enhanced label-free Raman spectroscopy for accurate serum-based detection of early-stage breast cancer via tumor-associated antigens
Abstract
Early-stage detection of breast cancer remains a critical challenge for improving clinical outcomes. In this study, an experimentally label-free Raman spectroscopy approach combined with principal component analysis and supervised machine learning is reported for the detection of tumor-associated antigen signatures in serum samples. Raman spectra from early-stage and advanced breast cancer patients, as well as healthy controls, were analyzed alongside recombinant tumor-associated antigen proteins, including α -1 antitrypsin, peptidyl-prolyl cis-trans isomerase A, peroxiredoxin-2, and triosephosphate isomerase-1. Distinct spectral variations were observed in lipid-, nucleic acid-, and protein-related bands. In particular, a characteristic peak at ∼ 519 cm−1 , associated with oxidized glutathione and oxidative stress-related structural alterations, was prominently observed in early-stage breast cancer and tumor-associated antigen spectra. Machine learning models trained on principal component analysis-reduced features, including linear discriminant analysis, support vector machine, decision tree, and Naïve Bayes, demonstrated high diagnostic performance, achieving ROC-AUC values above 0.94 for early-stage breast cancer versus controls and above 0.85 for early- versus advanced-stage discrimination. Furthermore, spectral and principal component analysis comparisons revealed partial clustering overlap between early-stage breast cancer samples and tumor-associated antigen proteins, supporting the biochemical relevance of the detected signatures. This integrated spectroscopic and computational approach provides a non-invasive and accurate strategy for early breast cancer detection and stage differentiation, highlighting its potential for clinical diagnostics.
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BibTeX
@article{akhtar2026machine,
title = {{Machine learning–enhanced label-free Raman spectroscopy for accurate serum-based detection of early-stage breast cancer via tumor-associated antigens}},
author = {Lariab Akhtar and Brenda Esmeralda Martínez-Zérega and David Omar Oseguera-Galindo and Luis Felipe Jave-Suárez and Adriana Aguilar-Lemarroy and Luis Adolfo Torres-González and Luis Arturo Palafox-Mariscal and Lourdes Giselle De la Torre-Gutiérrez and José Luis González-Solís},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76518-5},
url = {https://www.nature.com/articles/s41598-026-76518-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine learning–enhanced label-free Raman spectroscopy for accurate serum-based detection of early-stage breast cancer via tumor-associated antigens
AU - Lariab Akhtar
AU - Brenda Esmeralda Martínez-Zérega
AU - David Omar Oseguera-Galindo
AU - Luis Felipe Jave-Suárez
AU - Adriana Aguilar-Lemarroy
AU - Luis Adolfo Torres-González
AU - Luis Arturo Palafox-Mariscal
AU - Lourdes Giselle De la Torre-Gutiérrez
AU - José Luis González-Solís
PY - 2026
DA - 2026/10/28
JO - Scientific Reports
DO - 10.1038/s41598-026-76518-5
UR - https://www.nature.com/articles/s41598-026-76518-5
AB - Early-stage detection of breast cancer remains a critical challenge for improving clinical outcomes. In this study, an experimentally label-free Raman spectroscopy approach combined with principal component analysis and supervised machine learning is reported for the detection of tumor-associated antigen signatures in serum samples. Raman spectra from early-stage and advanced breast cancer patients, as well as healthy controls, were analyzed alongside recombinant tumor-associated antigen proteins, including α -1 antitrypsin, peptidyl-prolyl cis-trans isomerase A, peroxiredoxin-2, and triosephosphate isomerase-1. Distinct spectral variations were observed in lipid-, nucleic acid-, and protein-related bands. In particular, a characteristic peak at ∼ 519 cm^−1 , associated with oxidized glutathione and oxidative stress-related structural alterations, was prominently observed in early-stage breast cancer and tumor-associated antigen spectra. Machine learning models trained on principal component analysis-reduced features, including linear discriminant analysis, support vector machine, decision tree, and Naïve Bayes, demonstrated high diagnostic performance, achieving ROC-AUC values above 0.94 for early-stage breast cancer versus controls and above 0.85 for early- versus advanced-stage discrimination. Furthermore, spectral and principal component analysis comparisons revealed partial clustering overlap between early-stage breast cancer samples and tumor-associated antigen proteins, supporting the biochemical relevance of the detected signatures. This integrated spectroscopic and computational approach provides a non-invasive and accurate strategy for early breast cancer detection and stage differentiation, highlighting its potential for clinical diagnostics.
SN - 2045-2322
ER -