{
  "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 $$\\alpha$$ -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 $$\\sim$$ 519 $$\\mathrm {~cm}^{-1}$$ , 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Lariab Akhtar"
    },
    {
      "name": "Brenda Esmeralda Martínez-Zérega"
    },
    {
      "name": "David Omar Oseguera-Galindo"
    },
    {
      "name": "Luis Felipe Jave-Suárez"
    },
    {
      "name": "Adriana Aguilar-Lemarroy"
    },
    {
      "name": "Luis Adolfo Torres-González"
    },
    {
      "name": "Luis Arturo Palafox-Mariscal"
    },
    {
      "name": "Lourdes Giselle De la Torre-Gutiérrez"
    },
    {
      "name": "José Luis González-Solís"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dtra9skq44qje.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76518-5",
  "fulltext_url": null,
  "id": "dtra9skq44qje",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76518-5",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1304|Spectroscopy Techniques in Biomedical and Chemical Research"
    ]
  ],
  "openalex": "W7224134250",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/dtra9skq44qje.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:34Z",
      "native_id": "10.1038/s41598-026-76518-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76518-5"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Spectroscopy Techniques in Biomedical and Chemical Research",
      "subfield": "biophysics"
    }
  ],
  "title": "Machine learning–enhanced label-free Raman spectroscopy for accurate serum-based detection of early-stage breast cancer via tumor-associated antigens",
  "title_text": "Machine learning–enhanced label-free Raman spectroscopy for accurate serum-based detection of early-stage breast cancer via tumor-associated antigens",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:34Z",
  "url": "https://research.draconitic.com/paper/dtra9skq44qje",
  "venue": "Scientific Reports",
  "versions": []
}