Statistical classification for Raman spectra of tumoral genomic DNA

TL;DR

This study demonstrates that combining Surface-Enhanced Raman Scattering with statistical analysis enables rapid, cost-effective discrimination between healthy and tumoral genomic DNA, offering a promising alternative to DNA sequencing.

Contribution

The paper introduces a novel combination of SERS spectroscopy with statistical methods for efficient cancer diagnostics using genomic DNA.

Findings

High accuracy in distinguishing tumoral from healthy DNA spectra

Both PCA and $ ext{l}^2$ distance methods proved effective

Synergistic approach enables fast, inexpensive cancer detection

Abstract

We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous droplets of genomic DNA deposited onto silver-coated silicon nanowires and we show that it is possible to efficiently discriminate between spectra of tumoral and healthy cells. To assess the robustness of the proposed technique, we develop two different statistical approaches, one based on the Principal Component Analysis of spectral data and one based on the computation of the $\ell^2$ distance between spectra. Both methods prove to be highly efficient and we test their accuracy via the so-called Cohen's $\kappa$ statistics. We show that the synergistic combination of the SERS spectroscopy and the statistical analysis methods leads to efficient and fast cancer diagnostic applications allowing a rapid and unexpansive discrimination between healthy and tumoral genomic DNA alternative to the more complex and expensive DNA sequencing.