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Article Open Access

Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene

  • Authors:
    • Yufan Zhou
    • Wei Liu
    • Zhixue Fang
    • Ke Zhou
    • Xing Huang
    • Shanzheng Lu
    • Weilin Zhang
    • Qiaocheng Wen
    • Xu Chen
    • Zhigang Xiao
  • View Affiliations / Copyright

    Affiliations: Department of General Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan 410005, P.R. China, Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan 410005, P.R. China
    Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 31
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    Published online on: November 12, 2025
       https://doi.org/10.3892/ol.2025.15384
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Abstract

Hepatocellular carcinoma (HCC) is one of the most prevalent tumors in the world and poses a considerable threat to global healthcare. The reprogramming of glucose metabolism in tumor cells has demonstrated a notable association with the genesis, advancement and resistance to the chemotherapy of malignancies. In the present study, 10 integrative machine learning algorithms were used to develop a glycolysis‑related signature (GRS) using four datasets. Several predictive approaches were used to evaluate the performance of GRS in predicting the immunology response. In addition, in vitro experiments were performed to explore the biological functions of monocarboxylic acid transporter 1 (MCT1) in HCC. The optimal GRS developed by the Least Absolute Shrinkage and Selection Operator algorithm served as a risk factor for patients with HCC. Patients with HCC and a high‑risk score experienced a poor prognosis, with the area under the curves of 1, 3 and 5‑year receiver operating characteristic curves being 0.777, 0.787 and 0.766, respectively. A low‑risk score indicated higher levels of CD8+ cytotoxic T cells and M1 macrophages, as well as an increased estimation of stromal and immune cells in malignant tumors score. Moreover, increased tumor mutational burden score and programmed cell death protein 1 and cytotoxic T‑lymphocyte‑associated protein 4 immunophenoscores, as well as decreased Tumor Immune Dysfunction and Exclusion and tumor escape scores were found in patients with HCC that had low‑risk scores. The IC50 values of docetaxel, oxaliplatin, crizotinib and osimertinib were lower in HCC cases with a high‑risk score. In addition, the gene set scores that were associated with angiogenesis and Notch signaling were higher in the high‑risk score group. Downregulation of MCT1 inhibited the proliferation, migration and invasion of HCC cells and promoted the apoptosis of HCC cells. In conclusion, the present study developed a novel GRS for HCC, serving as an indicator for predicting clinical outcomes and responses to immunotherapy.
View Figures

Figure 1

Construction and validation of the
GRS. (A) C-index values of prognostic models developed using 10
different machine learning algorithms in training and validation
datasets. Kaplan-Meier survival curves and corresponding
time-dependent receiver operating characteristic curves for
patients with high vs. low GRS in (B) TCGA, (C) ICGC, (D) GSE14520
and (E) GSE76427 cohorts. GRS, glycolysis-related signature; TCGA,
The Cancer Genome Atlas; ICGC, International Cancer Genome
Consortium; AUC, area under the curve.

Figure 2

Assessment of the predictive
performance of the GRS. (A) Univariate and (B) multivariate Cox
regression analyses identified risk factors associated with HCC
prognosis. C-index curves comparing GRS with clinical parameters
across (C) TCGA, (D) ICGC, (E) GSE14520 and (F) GSE76427 training
and testing datasets. (G) Comparison of C-index values between GRS
and previously published HCC prognostic models. (H) Prognostic
nomogram integrating age, sex, clinical stage and GRS score. (I)
Calibration plots showing agreement between predicted and actual 1,
3 and 5-year survival probabilities. GRS, glycolysis-related
signature; HCC, hepatocellular carcinoma; TCGA, The Cancer Genome
Atlas; ICGC, International Cancer Genome Consortium; HR, hazard
ratio; OS, overall survival.

Figure 3

Tumor immune microenvironment
characteristics based on the GRS. (A) Correlation between GRS and
immune cell infiltration assessed by multiple deconvolution
algorithms. Differential levels of (B) CD8+ T cells, (C)
M1 macrophages and (D) M2 macrophages across GRS subgroups. (E)
Distribution of immune cell types and (F) immune-related functional
pathways based on single sample gene set enrichment analysis. (G)
Immune score, (H) stromal score and (I) ESTIMATE scores were
significantly elevated in the low-risk group compared with the
high-risk group. *P<0.05, **P<0.01 and ***P<0.001. GRS,
glycolysis-related signature; ESTIMATE, estimation of stromal and
immune cells in malignant tumors.

Figure 4

Predictive role of the GRS in
immunotherapy responses among patients with hepatocellular
carcinoma. (A) TMB and (B) immunophenoscore distributions between
high and low-GRS groups. (C and D) Differences in immune escape
metrics including TIDE, T cell dysfunction and exclusion scores.
(E) Expression levels of HLA-associated genes and (F) immune
checkpoint molecules across risk groups. Immunotherapy response
rate and survival outcomes in (G) GSE91061 and (H) IMvigor210
cohorts stratified by GRS. *P<0.05, **P<0.01 and
***P<0.001. GRS, glycolysis-related signature; TMB, tumor
mutational burden; HLA, human leukocyte antigen; TIDE, Tumor Immune
Dysfunction and Exclusion; CTLA, cytotoxic T-lymphocyte associated
protein 4; CR, complete response; PR; partial response; SD, stable
disease; PD, progressive disease.

Figure 5

GRS-associated differences in drug
sensitivity profiles. Estimated IC50 values of commonly
used (A) chemotherapeutic and (B) targeted agents in high-risk vs.
low-risk hepatocellular carcinoma groups, revealing enhanced
sensitivity in patients with high-GRS. GRS, glycolysis-related
signature.

Figure 6

Enrichment of oncogenic pathways in
patients with high-GRS hepatocellular carcinoma. Pathway scores for
(A) angiogenesis, (B) DNA repair, (C) EMT, (D) glycolysis, (E)
hypoxia, (F) IL2-STAT5 signaling, (G) mTORC1 signaling, (H) Notch
signaling and (I) the p53 pathway were elevated in high-risk
individuals. (J) Apoptosis-related gene set activity also increased
in the high-GRS subgroup. GRS, glycolysis-related signature; EMT,
epithelial mesenchymal transition.

Figure 7

Biological functions of MCT1 in HCC.
(A) Western blotting was applied to detect MCT1 expression levels
in normal hepatic and HCC cell lines. (B) Western blotting was
applied to detect MCT1 knockdown efficiency in MHCC97H cells. (C)
Western blotting was applied to detect EMT-related proteins
expression levels, including E-cadherin and N-cadherin protein. (D)
Colony formation assay showed colony formation was impaired
following MCT1 silencing. Flow cytometry showed reduced MCT1
expression caused (E) cell cycle arrest and (F) increased
apoptosis. (G) Cell migratory detected by wound healing assay and
(H) invasive capacities detected by Transwell assay were diminished
upon MCT1 downregulation. *P<0.05 and **P<0.01. MCT1,
monocarboxylic acid transporter 1; HCC, hepatocellular carcinoma;
E-cadherin, epithelial cadherin; N-cadherin, neural cadherin; sh,
short hairpin; Ctrl; control; ns, not significant.
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Copy and paste a formatted citation
Spandidos Publications style
Zhou Y, Liu W, Fang Z, Zhou K, Huang X, Lu S, Zhang W, Wen Q, Chen X, Xiao Z, Xiao Z, et al: Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene. Oncol Lett 31: 31, 2026.
APA
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S. ... Xiao, Z. (2026). Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene. Oncology Letters, 31, 31. https://doi.org/10.3892/ol.2025.15384
MLA
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S., Zhang, W., Wen, Q., Chen, X., Xiao, Z."Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene". Oncology Letters 31.1 (2026): 31.
Chicago
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S., Zhang, W., Wen, Q., Chen, X., Xiao, Z."Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene". Oncology Letters 31, no. 1 (2026): 31. https://doi.org/10.3892/ol.2025.15384
Copy and paste a formatted citation
x
Spandidos Publications style
Zhou Y, Liu W, Fang Z, Zhou K, Huang X, Lu S, Zhang W, Wen Q, Chen X, Xiao Z, Xiao Z, et al: Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene. Oncol Lett 31: 31, 2026.
APA
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S. ... Xiao, Z. (2026). Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene. Oncology Letters, 31, 31. https://doi.org/10.3892/ol.2025.15384
MLA
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S., Zhang, W., Wen, Q., Chen, X., Xiao, Z."Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene". Oncology Letters 31.1 (2026): 31.
Chicago
Zhou, Y., Liu, W., Fang, Z., Zhou, K., Huang, X., Lu, S., Zhang, W., Wen, Q., Chen, X., Xiao, Z."Machine learning‑based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis‑related gene". Oncology Letters 31, no. 1 (2026): 31. https://doi.org/10.3892/ol.2025.15384
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