Surface-Enhanced Raman Spectroscopy Applications in Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) harnesses the intense electromagnetic fields generated at the surface of plasmonic nanostructures to amplify the weak inelastic scattering signals of molecular vibrations. In oncology, SERS has emerged as a powerful, label-free technique for the detection and characterisation of cancer biomarkers in tissues and body fluids. By exploiting gold or silver nanoparticles as substrates, researchers obtain distinct spectral fingerprints of nucleic acids, proteins, lipids and other metabolites associated with tumourigenesis. The high sensitivity and spectral multiplexing capacity of SERS enable simultaneous detection of multiple targets at trace concentrations, offering new avenues for early diagnosis, treatment monitoring and intraoperative guidance. Integration with multivariate statistical methods and machine learning algorithms further refines spectral interpretation, enhancing diagnostic accuracy and robustness. Across diverse cancer types—colorectal, bladder, liver, breast and nasopharyngeal—SERS platforms have demonstrated the ability to distinguish malignant from healthy samples with high sensitivity and specificity. Recent advances in substrate design, from ordered silicon–nanoparticle arrays to novel nanoparticle morphologies, have improved reproducibility and stability of measurements. Collectively, these developments underscore the global significance of SERS as a non-invasive, rapid and highly specific tool poised to complement or even supplant conventional biopsy and imaging modalities in personalised cancer care.

Research from Nature Portfolio

A seminal study on bladder cancer employed serum SERS coupled with genetic algorithms and linear discriminant analysis to achieve non-invasive classification of patients versus healthy controls. Metallic nanoparticles enhanced key Raman bands attributed to proteins, nucleic acids and lipids, and the diagnostic model attained over 90% sensitivity and 100% specificity. This work demonstrated the feasibility of integrating feature-selection algorithms with SERS spectra to improve diagnostic performance beyond conventional principal component analysis approaches.

Surface-Enhanced Raman Spectroscopy Applications in Cancer Diagnosis publication trend

The graph below shows the total number of articles in surface-enhanced raman spectroscopy applications in cancer diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Surface-Enhanced Raman Spectroscopy (SERS): A vibrational spectroscopy technique that uses plasmonic nanostructures to amplify Raman signals from molecules near the metal surface.

Plasmonic nanoparticle: A metal nanoparticle—typically gold or silver—that supports collective oscillations of conduction electrons (plasmons), creating enhanced electromagnetic fields.

Liquid biopsy: A minimally invasive method for sampling and analysing circulating tumour biomarkers—such as cells, DNA or exosomes—from bodily fluids.

Principal Component Analysis (PCA): A multivariate statistical method that reduces data dimensionality by identifying orthogonal axes of maximum variance.

Linear Discriminant Analysis (LDA): A classification technique that projects data onto a lower-dimensional space to maximise separation between predefined groups.

References

1. Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms. Scientific Reports (2015).
2. Early cancer detection by SERS spectroscopy and machine learning. Light: Science & Applications (2023).
3. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. Advanced Science (2023).
4. Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy. Theranostics (2019).
5. Cancer Diagnosis through SERS and Other Related Techniques. International Journal of Molecular Sciences (2020).