Iron Oxide-Based Surface-Enhanced Raman Spectroscopy Bioprobes for Detecting Tumor Cells in Serous Effusion

Serous effusions, including pleural effusion and ascites, commonly occur in advanced cancers like lung and ovarian carcinomas. Detecting tumor cells in these effusions is crucial for assessing cancer metastasis. However, clinical methods mainly include cytological examination, which has limited sensitivity, and complex cell block technology that requires large volumes of serous effusion. Surface-enhanced Raman spectroscopy (SERS), with its high sensitivity and noninvasive nature, has emerged as a crucial tool for liquid biopsies. Building on this technology, a novel SERS bioprobe was specifically designed for the precise identification and capture of tumor cells in serous effusions, utilizing a composite material. The SERS bioprobes offer strong SERS enhancement, excellent spectral reproducibility, and molecular targeting, thereby enhancing detection specificity. Furthermore, SERS classification models were established to categorize samples based on tumor cell concentrations in serous effusions, enabling semiquantitative diagnostic capability. Notably, machine learning-assisted analysis enables rapid processing and classification of numerous Raman spectra and thorough feature extraction and greatly improves the SERS bioprobe's diagnostic accuracy. Consequently, the combination of SERS bioprobes and machine learning provides a rapid and effective detection method that overcomes the low sensitivity of conventional cytological detection in serous effusions and enables assessment of tumor cell concentration ranges within these fluids.

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Journal
Analytical Chemistry
Published
2026-01-20
DOI
https://doi.org/10.1021/acs.analchem.5c07446
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
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article

Iron Oxide-Based Surface-Enhanced Raman Spectroscopy Bioprobes for Detecting Tumor Cells in Serous Effusion

Jiaqi Lin, Yujiao Xie, Aiguo Wu, Li Sun et al.
Analytical Chemistry
Spectroscopy Techniques in Biomedical and Chemical Research
article

Iron Oxide-Based Surface-Enhanced Raman Spectroscopy Bioprobes for Detecting Tumor Cells in Serous Effusion

Jiaqi Lin, Yujiao Xie, Aiguo Wu, Li Sun, Tianxiang Chen, Jing Wang, Xinyu Miao, Tianan Jiang, Xiawei Xu, Zhiwei Hou, Aochi Liu, Lei Xu
article en

Abstract

Serous effusions, including pleural effusion and ascites, commonly occur in advanced cancers like lung and ovarian carcinomas. Detecting tumor cells in these effusions is crucial for assessing cancer metastasis. However, clinical methods mainly include cytological examination, which has limited sensitivity, and complex cell block technology that requires large volumes of serous effusion. Surface-enhanced Raman spectroscopy (SERS), with its high sensitivity and noninvasive nature, has emerged as a crucial tool for liquid biopsies. Building on this technology, a novel SERS bioprobe was specifically designed for the precise identification and capture of tumor cells in serous effusions, utilizing a composite material. The SERS bioprobes offer strong SERS enhancement, excellent spectral reproducibility, and molecular targeting, thereby enhancing detection specificity. Furthermore, SERS classification models were established to categorize samples based on tumor cell concentrations in serous effusions, enabling semiquantitative diagnostic capability. Notably, machine learning-assisted analysis enables rapid processing and classification of numerous Raman spectra and thorough feature extraction and greatly improves the SERS bioprobe's diagnostic accuracy. Consequently, the combination of SERS bioprobes and machine learning provides a rapid and effective detection method that overcomes the low sensitivity of conventional cytological detection in serous effusions and enables assessment of tumor cell concentration ranges within these fluids.

Analytical ChemistryVol. 98(4)
Chinese Academy of Sciences (CN), First Affiliated Hospital of Xiamen University (CN), Materials Technology (United Kingdom) (GB), Ningbo Institute of Industrial Technology (CN), Zhejiang University (CN)
Good health and well-being
Openalex Percentile: Top 84%
Spectroscopy Techniques in Biomedical and Chemical Research
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