An inherited predisposition allele promotes gastric cancer via enhancing deubiquitination-mediated activation of epithelial-to-mesenchymal transition signaling
Bolin Tao, … , Fei Xavier Chen, Mengyun Wang
Bolin Tao, … , Fei Xavier Chen, Mengyun Wang
Published February 25, 2025
Citation Information: J Clin Invest. 2025;135(8):e179617. https://doi.org/10.1172/JCI179617.
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Research Article Genetics Oncology

An inherited predisposition allele promotes gastric cancer via enhancing deubiquitination-mediated activation of epithelial-to-mesenchymal transition signaling

Abstract

Genome-wide human genetic studies have identified inherited cis-regulatory loci variants that predispose to cancers. However, the mechanisms by which these germline variants influence cancer progression, particularly through gene expression and proteostasis control, remain unclear. By analyzing genomic data from a gastric cancer (GC) case-control study (2,117 individuals), focusing on the ubiquitin-specific protease (USP) family, we identify the SNP rs72856331 (G>A) in the promoter region of the proto-oncogene USP47 as a putative susceptibility allele for GC. Mechanistically, the risk allele G is associated with enhanced USP47 expression, mediated by altered recruitment of the transcription factor GLI3 and changes in the epigenetic status at promoter. CRISPR/Cas9-mediated single-nucleotide conversion into risk allele G results in increased GLI3 binding and subsequent USP47 upregulation. The depletion of GLI3 results in a reduction of cancer-related phenotypes, similar to those observed following USP47 knockdown. Furthermore, we identify Snai1 as a deubiquitination target of USP47, explaining USP47-dependent activation of the epithelial-mesenchymal transition pathway and tumor progression. Our findings identify an important genetic predisposition that implicates the perturbation of transcription and proteostasis programs in GC, offering insights into prevention and therapeutic strategies for genetically stratified patients.

Authors

Bolin Tao, Zhenning Wang, Xuanyi Wang, Aixia Song, Jiaxian Liu, Jianan Wang, Qin Zhang, Zhaolin Chen, Zixian Wang, Wenjie Xu, Menghong Sun, Yanong Wang, Ping Zhang, Tao Xu, Gong-Hong Wei, Fei Xavier Chen, Mengyun Wang

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Figure 1

Identification of rs72856331 as a GC risk variant and correlation analysis of USP47 expression and clinical features.

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Identification of rs72856331 as a GC risk variant and correlation analys...
(A) Flowchart of the study design. (B) LD plots for 2 GC risk-associated SNPs in Asian populations (Han Chinese in Beijing and Japanese in Tokyo from 1000 Genomes Project; top panel). Functional annotations include RegulomeDB and HaploReg, version 4.2, scores. Regional association plot shows SNP associations (−log10 P) within 50 kb of rs72856331/USP47. Logistic regression assessed SNP GC risk associations (n = 1,076 cases, 1,041 controls). rs72856331 (index SNP) is highlighted in purple; r2 values of surrounding SNPs are color coded. Gene annotations are shown with directional arrows. (C) The TCGA STAD dataset showed significantly elevated USP47 expression in GC tissues. P value assessed by a 2-tailed Student’s t test. (D) In-house dataset showed significant upregulation of USP47 mRNA expression in GC tumors compared with matched adjacent normal tissues from the same individual by RT-qPCR. Data are represented as means ± SEM, analyzed using a 2-tailed Student’s t test. (E) Western blot indicated an apparent increase in USP47 protein levels in GC tumors compared with adjacent normal tissues from the same individual. (F–H) Elevated USP47 levels are substantially associated with GC clinical features indicated by increased tumor invasiveness (n = 406) (F); lymph node metastasis (n = 394) (G); and advanced tumor stages (n = 390) (H) in TCGA STAD cohort. Statistical significance determined by Kruskal-Wallis H test. (I) Higher USP47 levels show a moderate association with high-grade tumors in Ooi_Gc cohort, analyzed by Kruskal-Wallis H test. (J and K) Elevated USP47 levels correlate with reduced overall survival time in TCGA STAD and in-house GC patient cohorts, as depicted by Kaplan-Meier curves. P values calculated using the log-rank test.