An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that physical properties, including structural conformation, stiffness, and shape, as well as biological processes, such as methylation, can be pivotal to recognize DNA modifications. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated--silicon nanowires, to investigate genomic DNA from subtypes of melanoma and colon cancers and to efficiently discriminate tumor and healthy cells, as well as the different tumor subtypes. The diagnostic information is obtained by performing label--free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat, and leveraging the classification ability of learning models to reveal the specific and distinct interaction of healthy and tumor DNA molecules with nanowires.
@article{arxiv.2302.08918,
title = {Learning models for classifying Raman spectra of genomic DNA from tumor subtypes},
author = {Giacomo Lancia and Claudio Durastanti and Cristian Spitoni and Ilaria De Benedictis and Antonio Sciortino and Emilio N. M. Cirillo and Mario Ledda and Antonella Lisi and Annalisa Convertino and Valentina Mussi},
journal= {arXiv preprint arXiv:2302.08918},
year = {2023}
}