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Metabolic network-based identification of plasma markers for non-small cell lung cancer

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Abstract

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

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Acknowledgements

We want to thank Prof. Wei Jiang at Nanjing University of Aeronautics and Astronautics for his valued suggestions and guidance of TCGA data analysis.

Funding

This research was supported by the National Natural Science Foundation of China (No. 81903992), the Youth Foundation of Jiangsu Commission of Health (No. Q2017004), and the Jiangsu Provincial Medical Youth Talent (No. QNRC2016656).

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Author notes

  1. Linling Guo and Linrui Li contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, Jaingsu, China

    Linling Guo, Linrui Li, Yuan Tian, Fengguo Xu, Zunjian Zhang & Yin Huang

  2. Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital of Jiangsu Province, Nanjing, 210009, Jiangsu, China

    Zhiyun Xu, Fanchen Meng & Shuai Zhang

  3. Center for Biological Technology, Anhui Agricultural University, Hefei, 230036, Anhui, China

    Huimin Guo

  4. Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China

    Peijia Liu & Peifang Liu

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  1. Linling Guo
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Correspondence to Shuai Zhang or Yin Huang.

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This study was approved by the Ethics Committees of the Cancer Hospital of Jiangsu Province and the Second Affiliated Hospital of Harbin Medical University. All procedures performed in studies involving human participants were following the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Guo, L., Li, L., Xu, Z. et al. Metabolic network-based identification of plasma markers for non-small cell lung cancer. Anal Bioanal Chem 413, 7421–7430 (2021). https://doi.org/10.1007/s00216-021-03699-5

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