2. Academic Validation
  3. Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis

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

  • Nat Commun. 2025 Mar 14;16(1):2511. doi: 10.1038/s41467-025-57858-8.
Yongqiang Zheng # 1,  Kai Yu # 1 2,  Jin-Fei Lin # 1 3,  Zhuoran Liang # 1,  Qingfeng Zhang 1,  Junteng Li 1,  Qi-Nian Wu 1,  Cai-Yun He 1,  Mei Lin 1,  Qi Zhao 1,  Zhi-Xiang Zuo 1,  Huai-Qiang Ju 1,  Rui-Hua Xu 4 5,  Ze-Xian Liu 6
Affiliations

Affiliations

  • 1 State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • 2 Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, 77030, USA.
  • 3 Department of Clinical Laboratory, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
  • 4 State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. xurh@sysucc.org.cn.
  • 5 Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou, 510060, China. xurh@sysucc.org.cn.
  • 6 State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. liuzx@sysucc.org.cn.
  • # Contributed equally.
PMID: 40087285 DOI: 10.1038/s41467-025-57858-8
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.

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