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Updated: Nov 19, 2025

Author Spotlight: Genetic Profiling for Fluorouracil Response in Gastric Cancer
Published on: May 10, 2024
Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
Dong-Yan Zhao1, Xi-Zhen Sun1, Shu-Kun Yao1
1Department of Gastroenterology, China-Japan Friendship Hospital, Beijing 100029, China.
Background:
Tumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated.
Aim:
To explore the landscape of mutation profiles and determine the correlation between TMB and microRNA (miRNA) expression in GC.
Methods:
Genomic, transcriptomic, and clinical data from The Cancer Genome Atlas were used to obtain mutational profiles and investigate the statistical correlation between mutational burden and the overall survival of GC patients. The difference in immune infiltration between high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. Furthermore, miRNAs differentially expressed between the high- and low-TMB subgroups were identified and the least absolute shrinkage and selection operator method was employed to construct a miRNA-based signature for TMB prediction. The biological functions of the predictive miRNAs were identified with DIANA-miRPath v3.0.
Results:
C>T single nucleotide mutations exhibited the highest mutation incidence, and the top three mutated genes were TTN, TP53, and MUC16 in GC. High TMB values (top 20%) were markedly correlated with better survival outcome, and multivariable regression analysis indicated that TMB remained prognostic independent of TNM stage, histological grade, age, and gender. Different TMB levels exhibited different immune infiltration patterns. Significant differences between the high- and low-TMB subgroups were observed in the infiltration of CD8+ T cells, M1 macrophages, regulatory T cells, and CD4+ T cells. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients. The predictive performance of the signature was confirmed in the testing and the whole set. Receiver operating characteristic curve analysis demonstrated the optimal performance of the signature. Finally, enrichment analysis demonstrated that the set of miRNAs was significantly enriched in many key cancer and immune-related pathways.
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