Zhou Dan, Wu Ming, Tan Qilong, Sun Liyang, Tu Yuanxing, Zheng Weifang, Zhu Yun, Yang Min, Hu Kejia, Hu Fang, Xu Xiaohang, Zhou Hanyi, Luo Tian, Yang Fangming, Li Fuqiang, Jin Xin, Tu Huakang, Li Wenyuan, Wu Kui, Wu Xifeng
Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China.
Nat Commun. 2025 Aug 9;16(1):7365. doi: 10.1038/s41467-025-62459-6.
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.
东亚人群中很大一部分与肺癌相关的基因元件仍未被识别,这凸显了大规模全基因组研究的必要性,特别是针对非编码调控的研究。我们对13722名中国个体进行了基于全基因组测序(WGS)的全基因组扫描,以识别与肺癌相关的调控元件。我们通过对现有东亚研究进行荟萃分析,验证了基于常见变异的基因座。整合297名中国人的肺组织基因组-转录组参考面板,我们建立了变异与肺癌的关联,突出了包括TP63和DCBLD1在内的基因。通过实施STAAR流程进行罕见变异聚集分析,我们在非编码调控背景下识别并重复验证了新基因,包括PARPBP、PLA2G4C和RITA1。采用基于深度学习的方法,揭示了肺癌相关基因的潜在上游调节因子,如TP53﹑MYC﹑ZEB1和NFKB1。这些发现为肺癌病因的非编码调控提供了关键见解,为干预提供了更多潜在靶点。