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Glycolytic Plasticity of Metastatic Lung Cancer Captured by Noninvasive 18F-FDG PET/CT and Serum 1H-NMR Analysis: An Orthotopic Murine Model Study

  • Yi Hsiu Chung
  • , Tsai Hsien Hung
  • , Ching Fang Yu
  • , Cheng Kun Tsai
  • , Chi Chang Weng
  • , Fujie Jhang
  • , Fang Hsin Chen
  • , Gigin Lin*
  • *Corresponding author for this work
  • Chang Gung Memorial Hospital
  • National Tsing Hua University
  • Chang Gung University

Research output: Contribution to journalJournal Article peer-review

8 Scopus citations

Abstract

We aim to establish a noninvasive diagnostic platform to capture early phenotypic transformation for metastasis using 18F-FDG PET and 1H-NMR-based serum metabolomics. Mice with implantation of NCI-H460 cells grew only primary lung tumors in the localized group and had both primary and metastatic lung tumors in the metastatic group. The serum metabolites were analyzed using 1H-NMR at the time of PET/CT scan. The glycolysis status and cell proliferation were validated by Western blotting and staining. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of SUVmean and serum metabolites in metastasis. In the metastatic mice, the SUVmean of metastatic tumors was significantly higher than that of primary lung tumors in PET images, which was supported by elevated glycolytic protein expression of HK2 and PKM2. The serum pyruvate level in the metastatic group was significantly lower than that in the localized group, corresponding to increased pyruvate-catalyzed enzyme and proliferation rates in metastatic tumors. In diagnosing localized or metastatic tumors, the areas under the ROC curves of SUVmean and pyruvate were 0.92 and 0.91, respectively, with p < 0.05. In conclusion, the combination of 18F-FDG PET and 1H-NMR-based serum metabolomics demonstrated the feasibility of a glycolytic platform for diagnosing metastatic lung cancers.

Original languageEnglish
Article number110
JournalMetabolites
Volume13
Issue number1
DOIs
StatePublished - 09 01 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • 1H-NMR
  • F-FDG
  • animal study
  • lung cancer
  • metastasis

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