Journal Article Ahead of print
PubMed: 42019807 DOI: 10.1016/j.jlr.2026.101046
Raman analysis of breast cancer-associated adipocytes: a chemometric pipeline for lipid biochemistry profiling.
J Lipid Res
Authors: Pooja Girish, Pascaline Bouzy, Emilie Buache, Catherine Muller, Charlotte Vaysse, Landry Blanc, Sebastien Legendre, Olivier Piot
Affiliations
- HORIBA FRANCE SAS, 455 Avenue Eugène Avinée, 59120, Loos, France; BioSpecT Unit, EA 7506, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51100, Reims, France.
- BioSpecT Unit, EA 7506, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51100, Reims, France.
- Microenvironment, Cancer and Adipocytes IPBS, 31000 Toulouse.
- Institut Universitaire du Cancer Oncopole, 31100 Toulouse.
- HORIBA FRANCE SAS, 455 Avenue Eugène Avinée, 59120, Loos, France.
- BioSpecT Unit, EA 7506, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51100, Reims, France.
Abstract
This study describes an integrated chemometric pipeline to analyse Raman spectra from breast tissue adipocytes, distinguishing Cancer associated adipocytes (CAAs) from normal adipocytes (NAs) and assessing the impact of obesity. Raman spectra were acquired from NAs and CAAs from the invasive front of breast tumor in 10 patients (5 normal weight, NW; 5 obese weight, OW). Extended Multiplicative Scatter Correction (EMSC) was adapted to correct carotenoid spectral interference. Random forest (RF) classifier was used for identifying discriminant wavenumbers and Uniform Manifold Approximation and Projection (UMAP) for visualization, with clustering quality assessed using silhouette scores. The results show the effectiveness of the pipeline in correcting the interferences and in identifying the key discriminant spectral regions. Informative wavenumbers highlighted differences in lipid unsaturation (C=C stretch at 1655 cm -1, =C-H stretching at 3010 cm -1 ), triglyceride composition (C=O stretching at 1745 cm -1) and chain packing (CH 2 stretching 2840-2880 cm -1), revealing greater biochemical heterogeneity in CAAs. In summary, this integrative approach of data processing and analysing provides an effective framework for studying subtle spectral differences in samples. The pipeline successfully distinguished CAA and NA phenotypes, establishing a foundation for identifying spectroscopic biomarkers of adipocyte pathological remodelling in breast cancer.
MeSH
Biochemistry, Breast Neoplasms, Neoplasms, Adipocytes, Breast