Learning models for classifying Raman spectra of genomic DNA from tumor subtypes
Publication date
2023-07-14
Authors
Lancia, Giacomo
Durastanti, Claudio
Spitoni, Cristian
De Benedictis, Ilaria
Sciortino, Antonio
Cirillo, Emilio N M
Ledda, Mario
Lisi, Antonella
Convertino, Annalisa
Mussi, Valentina
Editors
Advisors
Supervisors
Document Type
Article
Metadata
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License
cc_by
Abstract
An early and accurate detection of different subtypes of tumors is crucial for an effective guidance to personalized therapy and in predicting the ability of tumor to metastasize. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated silicon nanowires (Ag/SiNWs), to efficiently discriminate genomic DNA of different subtypes of melanoma and colon tumors. 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 physico-chemical interaction of tumor DNA molecules with the Ag/NW, here supposed to be partly caused by a different DNA methylation degree.
Keywords
Methylation, Spectroscopy, General, SDG 3 - Good Health and Well-being
Citation
Lancia, G, Durastanti, C, Spitoni, C, De Benedictis, I, Sciortino, A, Cirillo, E N M, Ledda, M, Lisi, A, Convertino, A & Mussi, V 2023, 'Learning models for classifying Raman spectra of genomic DNA from tumor subtypes', Scientific Reports, vol. 13, no. 1, 11370. https://doi.org/10.1038/s41598-023-37303-w