Non-coding genetic elements of lung cancer identified using whole genome sequencing in 13,722 Chinese

Dan Zhou, Ming Wu, Qilong Tan, Liyang Sun, Yuanxing Tu, Weifang Zheng, Yun Zhu, Min Yang, Kejia Hu, Fang Hu, Xiaohang Xu, Hanyi Zhou, Tian Luo, Fangming Yang, Fuqiang Li, Xin Jin, Huakang Tu, Wenyuan Li, Kui Wu () and Xifeng Wu ()
Additional contact information
Dan Zhou: Zhejiang University School of Medicine
Ming Wu: Zhejiang University School of Medicine
Qilong Tan: Zhejiang University School of Medicine
Liyang Sun: Lanxi Branch (Lanxi People’s Hospital)
Yuanxing Tu: Lanxi Branch (Lanxi People’s Hospital)
Weifang Zheng: Lanxi Hospital of Traditional Chinese Medicine
Yun Zhu: Zhejiang University School of Medicine
Min Yang: Zhejiang University School of Medicine
Kejia Hu: Zhejiang University School of Medicine
Fang Hu: Zhejiang University School of Medicine
Xiaohang Xu: Zhejiang University School of Medicine
Hanyi Zhou: Zhejiang University School of Medicine
Tian Luo: Chinese Academy of Sciences (CAS)
Fangming Yang: Chinese Academy of Sciences (CAS)
Fuqiang Li: Chinese Academy of Sciences (CAS)
Xin Jin: BGI Research
Huakang Tu: Zhejiang University School of Medicine
Wenyuan Li: Zhejiang University School of Medicine
Kui Wu: Chinese Academy of Sciences (CAS)
Xifeng Wu: Zhejiang University School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract A substantial portion of lung cancer-associated genetic elements in East Asian populations remains unidentified, underscoring the need for large-scale genome-wide studies, particularly on non-coding regulation. We conducted a whole genome sequencing (WGS)-based genome-wide scan in 13,722 Chinese individuals to identify regulatory elements associated with lung cancer. We verified common-variant-based loci by meta-analysis across the available East Asian studies. Integrating a genome-transcriptome reference panel of lung tissue in 297 Chinese, we bridged the variant-lung cancer associations, highlighting genes including TP63 and DCBLD1. Implementing the STAAR pipeline for rare variant aggregate analysis, we identified and replicated novel genes, including PARPBP, PLA2G4C, and RITA1 in the context of non-coding regulation. Adapting a deep learning-based approach, potential upstream regulators such as TP53, MYC, ZEB1, and NFKB1 were revealed for the lung cancer-associated genes. These findings offered crucial insights into the non-coding regulation for the etiology of lung cancer, providing additional potential targets for intervention.

Date: 2025
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DOI: 10.1038/s41467-025-62459-6

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