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NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Author

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  • Yan Zhao

    (Second Affiliated Hospital of Shantou University Medical College
    Clinical Research Center, Shantou Central Hospital)

  • Changchun Ma

    (Cancer Hospital of Shantou University Medical College)

  • Rongzhi Cai

    (Second Affiliated Hospital of Shantou University Medical College)

  • Lijing Xin

    (Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne)

  • Yongsheng Li

    (Chongqing University Cancer Hospital)

  • Lixin Ke

    (Second Affiliated Hospital of Shantou University Medical College)

  • Wei Ye

    (Second Affiliated Hospital of Shantou University Medical College)

  • Ting Ouyang

    (Second Affiliated Hospital of Shantou University Medical College)

  • Jiahao Liang

    (Second Affiliated Hospital of Shantou University Medical College)

  • Renhua Wu

    (Second Affiliated Hospital of Shantou University Medical College)

  • Yan Lin

    (Second Affiliated Hospital of Shantou University Medical College)

Abstract

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabolomics on 1,153 matched ESCC tissues, normal mucosae, pre- and one-week post-operative sera and urines from 560 participants across three hospitals, with machine learning and WGCNA. Aberrations in ‘alanine, aspartate and glutamate metabolism’ proved to be prevalent throughout the ESCC evolution, consistently identified by NMR and MS, and reflected in 16 serum and 10 urine metabolic signatures in both discovery and validation sets. NMR-based simplified panels of any five serum or urine metabolites outperform clinical serological tumor markers (AUC = 0.984 and 0.930, respectively), and are effective in distinguishing early-stage ESCC in test set (serum accuracy = 0.994, urine accuracy = 0.879). Collectively, NMR-based biofluid screening can reveal characteristic metabolic events of ESCC and be feasible for early detection (ChiCTR2300073613).

Suggested Citation

  • Yan Zhao & Changchun Ma & Rongzhi Cai & Lijing Xin & Yongsheng Li & Lixin Ke & Wei Ye & Ting Ouyang & Jiahao Liang & Renhua Wu & Yan Lin, 2024. "NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46837-0
    DOI: 10.1038/s41467-024-46837-0
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    References listed on IDEAS

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    1. Sonia Tarazona & Leandro Balzano-Nogueira & David Gómez-Cabrero & Andreas Schmidt & Axel Imhof & Thomas Hankemeier & Jesper Tegnér & Johan A. Westerhuis & Ana Conesa, 2020. "Harmonization of quality metrics and power calculation in multi-omic studies," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. De-Chen Lin, 2023. "Large-scale genomic analyses reveal alterations and mechanisms underlying clonal evolution and immune evasion in esophageal cancer," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    3. Chuansheng Guo & Zhiyuan You & Hao Shi & Yu Sun & Xingrong Du & Gustavo Palacios & Cliff Guy & Sujing Yuan & Nicole M. Chapman & Seon Ah Lim & Xiang Sun & Jordy Saravia & Sherri Rankin & Yogesh Dhunga, 2023. "SLC38A2 and glutamine signalling in cDC1s dictate anti-tumour immunity," Nature, Nature, vol. 620(7972), pages 200-208, August.
    4. Heli Julkunen & Anna Cichońska & Mika Tiainen & Harri Koskela & Kristian Nybo & Valtteri Mäkelä & Jussi Nokso-Koivisto & Kati Kristiansson & Markus Perola & Veikko Salomaa & Pekka Jousilahti & Annamar, 2023. "Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
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    1. Shiyu Zhang & Zheng Wang & Yijing Wang & Yixiao Zhu & Qiao Zhou & Xingxing Jian & Guihu Zhao & Jian Qiu & Kun Xia & Beisha Tang & Julian Mutz & Jinchen Li & Bin Li, 2024. "A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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