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<p>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 <i>TP63</i> and <i>DCBLD1</i>. Implementing the STAAR pipeline for rare variant&#xa0;aggregate analysis, we identified and replicated novel genes, including <i>PARPBP</i>, <i>PLA2G4C</i>, and <i>RITA1</i> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月9日

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  • 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,
  • Xifeng Wu

摘要

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.