Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology

Xinyu Miao^{¹^,²^,^§}, Lei Xu^{¹^,²^,^§}, Li Sun^{¹^,²^,^§}, Yujiao Xie^{¹^,²}(), Jiahao Zhang^{¹^,²}, Xiawei Xu^{¹^,²}, Yue Hu^{¹^,²}, Zhouxu Zhang^{¹^,²}, Aochi Liu^{¹^,²}, Zhiwei Hou^{¹^,²}, Aiguo Wu^{¹^,²}(), Jie Lin^{¹^,²}()

1Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Laboratory of Advanced Theranostic Materials and Technology, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China

2Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China

^§These authors contributed equally to this work.

Show Author Information

Graphical Abstract

View original image Download original image

Abstract

Breast cancer has always been a research hotspot in the medical field due to its highest incidence and mortality rates among women worldwide. However, the significant molecular heterogeneity of breast cancer presents major challenges for its diagnosis and treatment. Surface-enhanced Raman spectroscopy (SERS) has gained considerable attention for its capability in trace detection and molecular analysis. To accurately identify different breast cancer cell subtypes, constructing reliable SERS bioprobes is essential. Therefore, a specific highly expressed receptor, human epidermal growth factor receptor 2 (HER-2), was employed to explore SERS bioprobes in this study. Two bioprobes capable of targeting breast cancer cells, Au NPs@4-MBA@PDA@aHER-2 and Au NPs@4-MPY@PDA@aHER-2, were synthesized. SERS performance testing indicated that the Au NPs were able to detect and trace molecules at concentrations as low as 2 × 10^–9 mol/L. Additionally, the two bioprobes exhibited good spectral stability with a relative standard deviation (RSD) of 9.58%. Moreover, by constructing a “symphonic SERS spectra” of the two bioprobes with prominent component analysis-linear discriminant analysis (PCA-LDA), the classification accuracy of distinguishing white blood cells (WBCs) and two breast cancer cell subtypes (SK-BR-3 and MDA-MB-231) reached up to 97.33%. The integration of machine learning with SERS detection provides a novel technological pathway for the early diagnosis and personalized treatment of breast cancer.

Keywords

surface-enhanced Raman spectroscopy (SERS)human epidermal growth factor receptor 2 (HER-2)breast cancer machine learning gold nanoparticles (Au NPs)bioprobes

Electronic Supplementary Material

Download File(s)

NBE-2024-0187_ESM.pdf (2.6 MB)

References

[1]

B. Han, R. Zheng, H. Zeng, et al. Cancer incidence and mortality in China, 2022. Journal of the National Cancer Center, 2010, 4: 47−53. https://doi.org/10.1016/j.jncc.2024.01.006