Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology
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 spectrosco...
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Tsinghua University Press
2025-03-01
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| Series: | Nano Biomedicine and Engineering |
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| Online Access: | https://www.sciopen.com/article/10.26599/NBE.2025.9290113 |
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| author | Xinyu Miao Lei Xu Li Sun Yujiao Xie Jiahao Zhang Xiawei Xu Yue Hu Zhouxu Zhang Aochi Liu Zhiwei Hou Aiguo Wu Jie Lin |
| author_facet | Xinyu Miao Lei Xu Li Sun Yujiao Xie Jiahao Zhang Xiawei Xu Yue Hu Zhouxu Zhang Aochi Liu Zhiwei Hou Aiguo Wu Jie Lin |
| author_sort | Xinyu Miao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-503d85d56b3d41a28ccc1a01ac6f7265 |
| institution | DOAJ |
| issn | 2097-3837 2150-5578 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Nano Biomedicine and Engineering |
| spelling | doaj-art-503d85d56b3d41a28ccc1a01ac6f72652025-08-20T02:42:53ZengTsinghua University PressNano Biomedicine and Engineering2097-38372150-55782025-03-0117112914210.26599/NBE.2025.9290113Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS TechnologyXinyu Miao0Lei Xu1Li Sun2Yujiao Xie3Jiahao Zhang4Xiawei Xu5Yue Hu6Zhouxu Zhang7Aochi Liu8Zhiwei Hou9Aiguo Wu10Jie Lin11Ningbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaNingbo 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, ChinaBreast 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.https://www.sciopen.com/article/10.26599/NBE.2025.9290113surface-enhanced raman spectroscopy (sers)human epidermal growth factor receptor 2 (her-2)breast cancermachine learninggold nanoparticles (au nps)bioprobes |
| spellingShingle | Xinyu Miao Lei Xu Li Sun Yujiao Xie Jiahao Zhang Xiawei Xu Yue Hu Zhouxu Zhang Aochi Liu Zhiwei Hou Aiguo Wu Jie Lin Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology Nano Biomedicine and Engineering surface-enhanced raman spectroscopy (sers) human epidermal growth factor receptor 2 (her-2) breast cancer machine learning gold nanoparticles (au nps) bioprobes |
| title | Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology |
| title_full | Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology |
| title_fullStr | Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology |
| title_full_unstemmed | Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology |
| title_short | Highly Sensitive Detection and Molecular Subtyping of Breast Cancer Cells Using Machine Learning-assisted SERS Technology |
| title_sort | highly sensitive detection and molecular subtyping of breast cancer cells using machine learning assisted sers technology |
| topic | surface-enhanced raman spectroscopy (sers) human epidermal growth factor receptor 2 (her-2) breast cancer machine learning gold nanoparticles (au nps) bioprobes |
| url | https://www.sciopen.com/article/10.26599/NBE.2025.9290113 |
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