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A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma

  • Authors:
    • Xia He
    • Rui Wang
    • Yonghua Zhu
    • Xi Chen
    • Yu Zhang
    • Min Sun
  • View Affiliations / Copyright

    Affiliations: Operating Theatre, Yixing Branch of Wuxi Medical Center of Nanjing Medical University, Yixing People's Hospital, Yixing, Jiangsu 214200, P.R. China, Department of Gastroenterology, Xuyi People's Hospital, Xuyi, Jiangsu 211700, P.R. China, Department of General Surgery, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, P.R. China, Department of Hepatopancreatobiliary Surgery, Yixing Branch of Wuxi Medical Center of Nanjing Medical University, Yixing People's Hospital, Yixing, Jiangsu 214200, P.R. China
    Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 403
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    Published online on: June 20, 2025
       https://doi.org/10.3892/ol.2025.15149
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Abstract

Glutamine has emerged as a focus of cancer metabolism research, although its role in liver hepatocellular carcinoma (LIHC) has yet to be fully elucidated. To determine the role of glutamine metabolism in the development of LIHC, the gene expression profiles and the clinical data of patients with LIHC were obtained from The Cancer Genome Atlas database and the International Cancer Genome Consortium website. Consensus clustering was used to identify distinct molecular clusters. Functional enrichment analysis between clusters was performed using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, and gene set variation analysis was performed. Least absolute shrinkage and selection operator and multivariate Cox regression analyses were then performed to generate a novel prognostic model. The prognostic, immune, mutational and drug‑sensitive characteristics of the model were subsequently evaluated. The clinical proteomic tumor analysis consortium and reverse transcription‑quantitative PCR analysis were then used to assess the protein and mRNA expression levels of the modeled genes. In addition, western blot analysis and Cell Counting Kit‑8, 5‑ethynyl‑2'‑deoxyuridine, Transwell and wound healing assays were performed to further evaluate the role of glutamate‑oxaloacetate transaminase 2 (GOT2) in the pathogenesis of LIHC. Data from multiple LIHC cohorts were utilized to identify two distinct clusters of LIHC, each characterized by unique clinical and immunological features associated with different levels of glutamine metabolism‑related genes. Numerous functional pathway differences were identified between these clusters, and these were demonstrated to be crucial for the onset and progression of LIHC. For modeling of glutamine metabolism‑related features, patients with LIHC were divided into two groups, namely a high‑ and a low‑risk group. Different clusters of patients with LIHC exhibited distinct characteristics in terms of their clinicopathological features, drug‑sensitivity and mutations. For example, the high‑risk group had a higher mutational load and was associated with a poorer prognosis compared to the low‑risk group. Finally, GOT2 protein and mRNA expression levels were significantly lower in LIHC tissues compared to paracancerous tissues, and GOT2 knockdown promoted the malignant phenotype of LIHC. In conclusion, the results of the present study indicate that glutamine metabolism exerts a crucial role in the tumorigenesis and progression of LIHC, and that this is positively associated with poor prognosis. The identified glutamine metabolism‑related signature was revealed to have notable accuracy in predicting the prognosis and immune characteristics of patients with LIHC. Moreover, the expression level of GOT2 was downregulated in LIHC, and a low expression of GOT2 was indicative of a poor prognosis for patients with LIHC, suggesting that the expression of GOT2 may be used as a potential therapeutic target.
View Figures

Figure 1

Expression and genetic variation of
glutamine metabolism-associated genes in LIHC. (A) Expression
difference of 21 glutamine metabolism-related genes between normal
and LIHC tissue. (B) Location of CNV alterations of glutamine
metabolism-related genes on 23 chromosomes. (C) CNV variation
frequency of glutamine metabolism-related genes. (D) Mutation
frequency of 21 glutamine metabolism-associated genes for 364
patients with LIHC in The Cancer Genome Atlas cohort. *P<0.05;
**P<0.01; ***P<0.001. LIHC, liver hepatocellular carcinoma;
CNV, copy number variation.

Figure 2

Establishment and biological
characteristics of glutamine metabolism-related clusters. (A) A
total of eight prognostic genes were selected using univariate Cox
regression analysis. (B) Consensus clustering matrix for K=2. (C)
Principal component analysis of clusters A and B. (D) Kaplan-Meier
overall survival curves for patients with LIHC in clusters A and B.
(E) Heatmap analysis of gene expression and clinical correlation
between clusters of LIHC. (F) KEGG pathway enrichment of DEGs
between clusters A and B. The enriched items were analyzed using
gene counts, gene ratios and adjusted P-values. (G) Gene Ontology
functional annotation analysis of DEGs between clusters A and B,
and enriched BPs, CCs and MFs. (H) gene set variation analysis
enrichment analysis, showing the biological pathways associated
with distinct glutamine metabolic modification patterns. Heatmap
analysis was used to visualize these BPs, with red representing
activated pathways and blue representing inhibited pathways. (I)
Sample distribution of different gene clusters. (J) Heatmap
analysis, showing gene expression and clinical correlation among
different gene clusters of LIHC. (K) Kaplan-Meier curves for
patients with LIHC in gene clusters A and B. (L) Expression
differences of 21 glutamine metabolism-related genes between gene
clusters A and B. *P<0.05; ***P<0.001. LIHC, liver
hepatocellular carcinoma; DEGs, differentially expressed genes;
KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological
process; CC, cellular component; MF, molecular functions; HR,
hazard ratio; CI, confidence interval; T, tumor; N, lymph node; M,
metastasis.

Figure 3

Immune infiltrate characteristics of
glutamine metabolism-related clusters. The abundance of the major
immunosuppressive infiltrating cells in the tumor microenvironment
in two clusters: (A) Macrophages; (B) regulatory T cells; and (C)
MDSCs. Cluster A was classified as the immunosuppressive phenotype,
characterized by the suppression of immunity. (D) Differential
expression of immune cells among the clusters. (E) ESTIMATE, (F)
stromal and (G) immune scores among the clusters. (H) Differential
expression of immune suppressive checkpoints among the clusters.
*P<0.05; **P<0.01; ***P<0.001. MDSC, myeloid-derived
suppressor cell; ESTIMATE, Estimation of Stromal and Immune cells
in Malignant Tumor tissues; ns, not significant; APC,
antigen-presenting cell; CCR, C-C Chemokine Receptor; HLA, Human
Leukocyte Antigen

Figure 4

Establishment of the GMS. (A)
Characteristics of changes in variable coefficients. (B) Selection
process of optimal values of parameter λ in the Lasso regression
model by cross-validation method. (C) A total of seven prognostic
genes were selected to construct the prognostic model. (D)
Differences in representative gene expression profiles and
clinicopathological characteristics between the low- and high-risk
score groups. (E) Kaplan-Meier survival analysis of the patients
with LIHC from TCGA. (F) Time-dependent ROC analysis of patients
with LIHC from TCGA. (G) ROC curve analysis in TCGA-LIHC cohort.
(H) Univariate Cox regression analyses of clinicopathological
features for predicting the survival rates of patients with LIHC
from TCGA. (I) Multivariate Cox regression analyses of
clinicopathological features for predicting the survival rates of
patients with LIHC from TCGA. (J) Kaplan-Meier survival analysis of
the patients with LIHC from the ICGC. (K) Time-dependent ROC
analysis of the patients with LIHC from the ICGC is shown. (L) ROC
curve analysis in the ICGC cohort. (M) Univariate and (N)
multivariate Cox regression analyses of clinicopathological
features for predicting the survival rates of patients with LIHC
from the ICGC. GOT2, glutamate-oxaloacetate transaminase 2; SF3B4,
Splicing factor 3B subunit 4; RPS7P1, Ribosomal Protein S7
Pseudogene 1; SLC66A1, Lysosomal amino acid transporter 1 homolog;
TMEM41B, Transmembrane protein 41B; YBX1, Y-box-binding protein 1;
CYB5R3, NADH-cytochrome b5 reductase 3; GMS, glutamine
metabolism-related signature; LIHC, liver hepatocellular carcinoma;
TCGA, The Cancer Genome Atlas; ROC, receptor operator
characteristic; ICGC, International Cancer Genome Consortium; HR,
hazard ratio; CI, confidence interval; T, tumor; N, lymph node; M,
metastasis; AUC, area under the curve.

Figure 5

Identification of clinicopathological
features of the glutamine metabolism-related signature. (A)
Alluvial diagram, showing the changes of clusters, gene clusters
and index. Differential expression of the risk score among (B)
clusters and (C) gene clusters. Higher risk scores were associated
with worse clinical parameters in patients with LIHC: (D)
Histological grade, (E) stage, (F) T stage, (G) N stage and (H) M
stage according to data from The Cancer Genome Atlas. Differences
in the proportion of cases with different (I) grades, (J) stages,
(K) T stages, (L) M stages and (M) N stages between the high- and
low-risk score groups. Kaplan-Meier survival analysis of patients
with LIHC, comparing between high- and low-risk groups in different
clinical groups: (N) Patients with G1-2 and G3-4, (O) patients with
SI–II and S3-4, (P) patients with T1-2 and T3-4, and (Q) patients
with M0 and N0. ***P<0.001. G, grade; T, tumor; N, lymph node;
M, metastasis.

Figure 6

Mutational and immunotherapeutic
characteristics of the GMS. (A) Differential expression of TMB
between high- and low-GMS groups. (B) Correlation analysis between
TMB and GMS. (C) Survival analysis between high- and low-TMB
groups. (D) Survival analysis of distinct groups stratified by both
TMB and GMS. (E) Waterfall plots of somatic mutations in tumors in
the high- and low-GMS groups. (F) Somatic mutations in tumors in
the high-GMS group. GMS, glutamine metabolism-related signature;
TMB, tumor mutation burden.

Figure 7

A total of six potential therapeutic
drugs in LIHC with differential IC50 values between
high- and low-glutamine metabolism-related signature groups.
Potential therapeutic drugs in LIHC: (A) Axitinib; (B) gefitinib;
(C) mitocycin C; (D) gemcitabine; (E) doxorubicin; and (F)
cisplatin. LIHC, liver hepatocellular carcinoma.

Figure 8

Identification of expression and
clinicopathological characteristics of the modeled genes. (A)
Candidate gene involved in Prognostic Model Genes and Glutamine
Metabolism Related Genes. (B) Relative expression of GOT2 between
LIHC tissues and normal tissues. (C) Protein expression of GOT2,
comparing between LIHC and normal tissues from CPTAC samples. (D)
Protein expression of GOT2, comparing between LIHC and normal
tissues from CPTAC samples. Association between GOT2 and different
clinical features: (E) grade, (F) stage, (G) T stage, (H) M stage,
(I) N stage and (J) survival status. Representative protein
expression of GOT2 in (K) normal liver and (L) LIHC tissue, as
retrieved from the Human Protein Atlas (scale bar, 100 µm).
*P<0.05; **P<0.01; ***P<0.001. GOT2,
glutamate-oxaloacetate transaminase 2; LIHC, liver hepatocellular
carcinoma; CPTAC, Clinical Proteomic Tumor Analysis Consortium; T,
tumor; N, normal; G, grade; M, metastasis; N, lymph node.

Figure 9

GOT2 knockdown significantly
increases LIHC cell migration and proliferation. (A) In three LIHC
and cell lines, the expression of GOT2 was semi-quantified using
western blot analysis. (B) Effectiveness of GOT2-siRNA knockdown.
(C) Growth kinetics of Huh7 and HCCLM3 cells, post-transfection
with si-NC or si-GOT2, was charted based on the CCK-8 assay data at
various time points–0, 24, 48, and 72 h. (D) HCCLM3 cells with GOT2
knockdown were used to assess cell proliferation using EdU assays
(scale bar, 50 µm). (E) Huh7 cells with GOT2 knockdown were used to
assess cell proliferation using EdU assay (scale bar, 50 µm). Huh7
and HCCLM3 cells with GOT2 expression knockdown were used to assess
migration using (F) Transwell (scale bar, 200 µm) and (G) wound
healing (scale bar, 50 µm) assays. **P<0.01; ***P<0.001.
GOT2, glutamate-oxaloacetate transaminase 2; LIHC, liver
hepatocellular carcinoma; si, small interfering; EdU,
5-ethynyl-2′-deoxyuridine; nc, negative control; OD, optical
density.
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Copy and paste a formatted citation
Spandidos Publications style
He X, Wang R, Zhu Y, Chen X, Zhang Y and Sun M: A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma. Oncol Lett 30: 403, 2025.
APA
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., & Sun, M. (2025). A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma. Oncology Letters, 30, 403. https://doi.org/10.3892/ol.2025.15149
MLA
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., Sun, M."A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma". Oncology Letters 30.3 (2025): 403.
Chicago
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., Sun, M."A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma". Oncology Letters 30, no. 3 (2025): 403. https://doi.org/10.3892/ol.2025.15149
Copy and paste a formatted citation
x
Spandidos Publications style
He X, Wang R, Zhu Y, Chen X, Zhang Y and Sun M: A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma. Oncol Lett 30: 403, 2025.
APA
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., & Sun, M. (2025). A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma. Oncology Letters, 30, 403. https://doi.org/10.3892/ol.2025.15149
MLA
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., Sun, M."A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma". Oncology Letters 30.3 (2025): 403.
Chicago
He, X., Wang, R., Zhu, Y., Chen, X., Zhang, Y., Sun, M."A novel glutamine metabolism‑related risk model for prognostic prediction of liver hepatocellular carcinoma". Oncology Letters 30, no. 3 (2025): 403. https://doi.org/10.3892/ol.2025.15149
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