Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

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, Yongxiang Song, Minglei Yang, Guofang Zhao, Bentong Yu, Minjie Ma, Lunxu Liu, Xiwen Sun, Yunlang She (), Dan Xie (), Deping Zhao () and Chang Chen ()
Additional contact information
Mengmeng Zhao: Tongji University School of Medicine
Gang Xue: Sichuan University
Bingxi He: Beihang University
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
Tao Chen: Tongji University School of Medicine
Jun Zhang: Tailai Inc
Ziyue Yan: Tailai Inc
Xinlei Hu: 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
Deping Zhao: Tongji University School of Medicine
Chang Chen: Tongji University School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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

Date: 2025
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DOI: 10.1038/s41467-024-55594-z

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