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Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis

Author

Listed:
  • Yongqiang Zheng

    (Sun Yat-sen University Cancer Center)

  • Kai Yu

    (Sun Yat-sen University Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Jin-Fei Lin

    (Sun Yat-sen University Cancer Center
    Sun Yat-Sen University Cancer Center)

  • Zhuoran Liang

    (Sun Yat-sen University Cancer Center)

  • Qingfeng Zhang

    (Sun Yat-sen University Cancer Center)

  • Junteng Li

    (Sun Yat-sen University Cancer Center)

  • Qi-Nian Wu

    (Sun Yat-sen University Cancer Center)

  • Cai-Yun He

    (Sun Yat-sen University Cancer Center)

  • Mei Lin

    (Sun Yat-sen University Cancer Center)

  • Qi Zhao

    (Sun Yat-sen University Cancer Center)

  • Zhi-Xiang Zuo

    (Sun Yat-sen University Cancer Center)

  • Huai-Qiang Ju

    (Sun Yat-sen University Cancer Center)

  • Rui-Hua Xu

    (Sun Yat-sen University Cancer Center
    Chinese Academy of Medical Sciences)

  • Ze-Xian Liu

    (Sun Yat-sen University Cancer Center)

Abstract

Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically characterize potential shuttling-attacking mutations (SAMs) across cancers through developing the deep learning model pSAM for the ab initio decoding of the sequence determinants of nucleocytoplasmic shuttling. Leveraging cancer mutations across 11 cancer types, we find that SAMs enrich functional genetic variations and critical genes in cancer. We experimentally validate a dozen SAMs, among which R14M in PTEN, P255L in CHFR, etc. are identified to disrupt the nuclear localization signals through interfering their interactions with importins. Further studies confirm that the nucleocytoplasmic shuttling altered by SAMs in PTEN and CHFR rewire the downstream signaling and eliminate their function of tumor suppression. Thus, this study will help to understand the molecular traits of nucleocytoplasmic shuttling and their dysfunctions mediated by genetic variants.

Suggested Citation

  • Yongqiang Zheng & Kai Yu & Jin-Fei Lin & Zhuoran Liang & Qingfeng Zhang & Junteng Li & Qi-Nian Wu & Cai-Yun He & Mei Lin & Qi Zhao & Zhi-Xiang Zuo & Huai-Qiang Ju & Rui-Hua Xu & Ze-Xian Liu, 2025. "Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57858-8
    DOI: 10.1038/s41467-025-57858-8
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