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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

Author

Listed:
  • Lin Huang

    (Shanghai Jiao Tong University)

  • Lin Wang

    (Shanghai Jiao Tong University)

  • Xiaomeng Hu

    (Shanghai Jiao Tong University)

  • Sen Chen

    (iMS Clinic)

  • Yunwen Tao

    (Southern Methodist University)

  • Haiyang Su

    (Shanghai Jiao Tong University)

  • Jing Yang

    (Shanghai Jiao Tong University)

  • Wei Xu

    (Shanghai Jiao Tong University)

  • Vadanasundari Vedarethinam

    (Shanghai Jiao Tong University)

  • Shu Wu

    (iMS Clinic)

  • Bin Liu

    (iMS Clinic)

  • Xinze Wan

    (iMS Clinic)

  • Jiatao Lou

    (Shanghai Jiao Tong University)

  • Qian Wang

    (Shanghai Jiao Tong University)

  • Kun Qian

    (Shanghai Jiao Tong University)

Abstract

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p

Suggested Citation

  • Lin Huang & Lin Wang & Xiaomeng Hu & Sen Chen & Yunwen Tao & Haiyang Su & Jing Yang & Wei Xu & Vadanasundari Vedarethinam & Shu Wu & Bin Liu & Xinze Wan & Jiatao Lou & Qian Wang & Kun Qian, 2020. "Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17347-6
    DOI: 10.1038/s41467-020-17347-6
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    References listed on IDEAS

    as
    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Hayley M. Dorfman & Samuel J. Gershman, 2019. "Controllability governs the balance between Pavlovian and instrumental action selection," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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    Cited by:

    1. Bangfeng Wang & Yiwei Li & Mengfan Zhou & Yulong Han & Mingyu Zhang & Zhaolong Gao & Zetai Liu & Peng Chen & Wei Du & Xingcai Zhang & Xiaojun Feng & Bi-Feng Liu, 2023. "Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Xiaohua Xing & Linsheng Cai & Jiahe Ouyang & Fei Wang & Zongman Li & Mingxin Liu & Yingchao Wang & Yang Zhou & En Hu & Changli Huang & Liming Wu & Jingfeng Liu & Xiaolong Liu, 2023. "Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Yao Yao & Xueping Wang & Jian Guan & Chuanbo Xie & Hui Zhang & Jing Yang & Yao Luo & Lili Chen & Mingyue Zhao & Bitao Huo & Tiantian Yu & Wenhua Lu & Qiao Liu & Hongli Du & Yuying Liu & Peng Huang & T, 2023. "Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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