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Cellular and Molecular Biology

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform

British Journal of Cancer (2024)Cite this article

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Abstract

Background

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis.

Methods

Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS).

Results

By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5–8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples.

Conclusion

This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

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Fig. 1: Raman spectra analysis and LDA visualisation of NPC and non-NPC cell lines.
Fig. 2: Biomolecular analysis based on single-cell Raman spectra.
Fig. 3: Raman images of individual cells from SUNE-1, 5-8F and 6-10B cell lines.
Fig. 4: Raman images of individual cells from NPEC-BM1, CNE1 and CNE2 cell lines.
Fig. 5: Machine learning classification of NPC and non-NPC cell lines.
Fig. 6: Machine learning classification of NPC and non-NPC tissue samples.

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Data availability

Datasets generated and analysed for this study are available from corresponding authors upon request.

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Acknowledgements

We thank Professor Musheng Zeng from Sun Yat-Sen University Cancer Center for providing cell lines used in this study.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 81772921). A grant from the Science and Technology Planning Project of Shenzhen City of China (JCYJ20230807142806014, JCYJ20190812171816857, JCYJ20180306172209668, JCYJ20190812171215641). Sanming Project of Medicine in Shenzhen (No. SZSM201601062). Shenzhen Key Medical Discipline (No. SZXK054). WEH thanks National Key R&D Program of China (2022YFC2403300) and Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences for financial support.

Author information

Author notes

  1. These authors contributed equally: Jiabao Xu, Dayang Chen.

  2. These authors jointly supervised this work: Wei E. Huang, Dan Xiong.

Authors and Affiliations

  1. Division of Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK

    Jiabao Xu

  2. Medical Laboratory of the Third Affiliated Hospital of Shenzhen University, Shenzhen, China

    Dayang Chen, Wei Wu, Xiang Ji, Xiaowen Dou, Xiaojuan Gao, Xiuming Zhang & Dan Xiong

  3. Department of Otolaryngology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Jian Li

  4. Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK

    Wei E. Huang

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  1. Jiabao Xu
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Contributions

Dan Xiong and Dayang Chen, established and supervised the study. Xiaowen Dou, Xiaojuan Gao and Xiuming Zhang designed analysis framework. Dan Xiong and Wei E. Huang designed Raman experiments. Wei Wu and Xiang Ji performed the experiments. Jiabao Xu performed data analysis and visualisation. Dan Xiong, Dayang Chen, Jiabao Xu and Wei E. Huang interpreted the data. Jiabao Xu wrote the original manuscript. Dan Xiong, Wei E. Huang made manuscript revisions. All authors reviewed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Wei E. Huang or Dan Xiong.

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The authors declare no competing interests.

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Written informed consent was obtained from the patients included in this study, and approvals from the First Affiliated Hospital of Sun Yat-sen University hospital were obtained to use the clinical samples (No. 2020-483).

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Xu, J., Chen, D., Wu, W. et al. A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02637-3

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