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Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

Author

Listed:
  • Mengmeng Zhao

    (Tongji University School of Medicine)

  • Gang Xue

    (Sichuan University
    Sichuan University)

  • Bingxi He

    (Beihang University
    Ministry of Industry and Information Technology
    Chinese Academy of Sciences)

  • Jiajun Deng

    (Tongji University School of Medicine)

  • Tingting Wang

    (Fudan University)

  • Yifan Zhong

    (Tongji University School of Medicine)

  • Shenghui Li

    (Tongji University School of Medicine)

  • Yang Wang

    (Tongji University School of Medicine)

  • Yiming He

    (Tongji University School of Medicine
    The First Affiliated Hospital of Nanchang University)

  • Tao Chen

    (Tongji University School of Medicine)

  • Jun Zhang

    (Tailai Inc)

  • Ziyue Yan

    (Tailai Inc)

  • Xinlei Hu

    (Sichuan University
    Sichuan University)

  • Liuning Guo

    (Zunyi Medical College)

  • Wendong Qu

    (Zunyi Medical College)

  • Yongxiang Song

    (Zunyi Medical College)

  • Minglei Yang

    (Chinese Academy of Sciences)

  • Guofang Zhao

    (Chinese Academy of Sciences)

  • Bentong Yu

    (The First Affiliated Hospital of Nanchang University)

  • Minjie Ma

    (The First Hospital of Lanzhou University)

  • Lunxu Liu

    (Sichuan University)

  • Xiwen Sun

    (Tongji University School of Medicine)

  • Yunlang She

    (Tongji University School of Medicine)

  • Dan Xie

    (Sichuan University
    Sichuan University)

  • Deping Zhao

    (Tongji University School of Medicine)

  • Chang Chen

    (Tongji University School of Medicine)

Abstract

Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p

Suggested Citation

  • Mengmeng Zhao & Gang Xue & Bingxi He & Jiajun Deng & Tingting Wang & Yifan Zhong & Shenghui Li & Yang Wang & Yiming He & Tao Chen & Jun Zhang & Ziyue Yan & Xinlei Hu & Liuning Guo & Wendong Qu & Yongx, 2025. "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55594-z
    DOI: 10.1038/s41467-024-55594-z
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