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A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.

Meijun Li·Zhiyi Chen·Xudong Shen·Zhenyu Feng·Shaohua Wei·Yecheng Li·Minjian Zhang·Zhenyu Ye
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Abstract

BACKGROUND: Glutamine dependence is a hallmark of tumor cell metabolism, and further molecular classification based on glutamine metabolism in patients with hepatocellular carcinoma (HCC) may provide clinical value. This study thus comprehensively examined the patterns of HCC-specific alterations in glutamine metabolism. METHODS: Consensus clustering analysis was conducted on samples from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset based on glutamine metabolism-related genes, which was validated in the GSE76427, the Liver Cancer-France (LICA-FR) cohort, and the Liver Cancer-Japan (LIRI-JP) cohort from the ICGC. Somatic mutation features were evaluated with the Maftools package in R. The activity of oncogenic pathways was estimated via gene set enrichment analysis (GSEA) or single-sample GSEA (ssGSEA). The tumor microenvironment was analyzed using both the CIBERSORT algorithm (for immune cell infiltration estimation) and the ESTIMATE algorithm (for stromal and immune score calculation). Drug sensitivity and immune checkpoint blockade (ICB) response were also analyzed, for which a classifier was built via least absolute shrinkage and selection operator (LASSO). Immunohistochemistry (IHC) was performed to validate the protein expression levels of key differentially expressed genes (DEGs). Intracellular glutamine content under different glutamine concentrations was measured. The viability of HCC cell lines under varying glutamine concentrations was assessed via Cell Counting Kit-8 (CCK-8) assays. Cell migration and invasion were evaluated through Transwell assays, and protein expression was analyzed via Western blotting. RESULTS: HCC samples were classified into two glutamine metabolism-based clusters, with cluster 1 having a more advanced stage of disease and shorter survival than cluster 2. A higher frequency of genetic mutations and stronger activation of oncogenic pathways was found in cluster 1. There were substantial differences in immune cell infiltration and stromal scores between clusters 1 and 2. Cluster 1 exhibited significantly higher infiltration of immunosuppressive cells and lower stromal scores compared to cluster 2. Cluster 1 had a stronger response to ICB due as indicated by a higher tumor mutation burden (TMB) and T cell-inflamed gene expression profile score, immune checkpoints, and Tumor Immune Dysfunction and Exclusion (TIDE)-predicted data. Moreover, the LASSO classifier accurately differentiated the two clusters. The DEGs between the two clusters were validated in clinical samples. IHC confirmed the differential expression of glutamine metabolism-related genes in HCC samples. CCK-8 assays showed no significant effect of glutamine concentration on cell proliferation. However, Transwell assays revealed that glutamine deprivation (0.2 mM) reduced migration and invasion, while high-glutamine conditions (10 mM) promoted them. Western blotting showed increased expression of metabolism-related proteins under high-glutamine conditions and reduced expression under deprivation. CONCLUSIONS: Altogether, these findings indicate the involvement of glutamine metabolism in HCC and may help inform patient stratification and the formulation of precision therapeutics for this population.

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Hepatocellular carcinoma (HCC)classificationglutamine metabolismheterogeneitytumor microenvironmentJournal Article

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Meijun Li, Zhiyi Chen, Xudong Shen, Zhenyu Feng, Shaohua Wei, Yecheng Li, Minjian Zhang, Zhenyu Ye. 2026-08-25. A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.. https://doi.org/10.21037/jgo-2026-0706

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