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Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases

  • Authors:
    • Ke Liu
    • Erbao Chen
    • Jiaming Liang
    • Yanyan Li
    • Binghua Cheng
    • Wenli Shi
    • Zeyu Zhou
    • Wenjie Zhou
    • Hui Tian
    • Dongye Yang
    • Ximing Shao
    • Hongchang Li
  • View Affiliations / Copyright

    Affiliations: Guangdong Key Laboratory of Nanomedicine, CAS‑HK Joint Lab of Biomaterials, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P.R. China, Department of Hepatobiliary and Pancreatic Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, P.R. China, Division of Gastroenterology and Hepatology, The University of Hong Kong‑Shenzhen Hospital, Shenzhen, Guangdong 518053, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 76
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    Published online on: July 1, 2025
       https://doi.org/10.3892/mco.2025.2871
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Abstract

Infection with the hepatitis B virus (HBV) is a key risk factor for hepatocellular carcinoma (HCC) development and progression. It is widely recognized that immunopathological mechanisms are pivotal in the development of HBV‑associated HCC; nevertheless, the specific underlying mechanisms through which HBV‑induced modifications within the tumor microenvironment (TME) contribute to HCC pathogenesis have yet to be fully elucidated. In the present study, single‑cell RNA sequencing was utilized to analyze and compare the immune landscapes between HBV‑positive and HBV‑negative HCC. These experiments revealed that HBV infection significantly modifies the composition and state of immune cells, leading to the suppression and exhaustion of T cells within the TME. Specifically, increases in the proportions of SLC4A10+ CD8+ T cells and IFITM3+ macrophages were observed, along with an upregulation of the gene SLC35F1 in various immune cell subtypes. Taken together, these findings have offered valuable insights into the alteration of the immunological microenvironment in HCC that is associated with HBV infection, suggesting possible targets for immunotherapeutic intervention.
View Figures

Figure 1

Analysis of T cell heterogeneity. (A)
UMAP plot of CD4+ T cells, with cell subtypes
distinguished according to different colors. (B) Stacked bar plots,
summarizing the proportions of CD4+ T cell subtypes in
HBV-negative and HBV-positive samples. (C) Dot plot, showing the
percentages of cells expressing marker genes alongside their
average expression levels across CD4+ T cell subtypes.
(D) Volcano plot, highlighting DEGs between HBV-negative and
HBV-positive CD4+ T cells. The P values were calculated
using the two-sided Wilcoxon rank-sum test, with Benjamini-Hochberg
adjustment. (E) UMAP plot of CD8+ T cells, with cell
subtypes color-coded. (F) Stacked bar plots, summarizing the
proportions of CD8+ T cell subtypes in HBV-negative and
HBV-positive samples. (G) Volcano plot of DEGs, comparing
HBV-negative and HBV-positive CD8+ T cells. The P-values
were generated using the two-sided Wilcoxon rank-sum test, with
Benjamini-Hochberg adjustment. (H) Violin plot, depicting the
expression of exhaustion marker genes in HBV-negative and
HBV-positive CD8+ T cells. Statistical significance was
calculated using unpaired two-tailed Student's t-test.
****P<0.0001. (I) Kaplan-Meier curves, illustrating
survival of patients with LIHC with varying levels of CD8-c7-PDCD1
infiltration. The P-value was calculated using the Renyi test. (J)
Heatmap, demonstrating normalized TF regulon activity in
CD4+ and CD8+ T cell subtypes, as predicted
by pySCENIC. UMAP, Unsupervised Uniform Manifold Approximation and
Projection; HBV, hepatitis B virus; DEGs, differentially expressed
genes; LIHC, liver hepatocellular carcinoma; TF, transcription
factor.

Figure 2

Analysis of macrophage and DC
heterogeneity. (A) UMAP plot of macrophages with subtypes, as
distinguished by different colors. (B) Stacked bar plots,
summarizing the proportions of macrophage subtypes in HBV-negative
and HBV-positive samples. (C) Dot plot, showing the percentages of
cells expressing marker genes alongside their average expression
levels across macrophage subtypes. (D) Volcano plot, highlighting
DEGs between HBV-negative and HBV-positive macrophages. The
P-values were calculated using the two-sided Wilcoxon rank-sum
test, with Benjamini-Hochberg adjustment. (E) UMAP plot of DCs,
with subtypes color-coded. (F) Stacked bar plots, summarizing the
proportions of DC subtypes in HBV-negative and HBV-positive
samples. (G) Dot plot, showing the percentages of cells expressing
marker genes alongside their average expression levels across DC
subtypes. (H) Volcano plot of DEGs, comparing HBV-negative and
HBV-positive DCs. The P-values were generated using the two-sided
Wilcoxon rank-sum test, with Benjamini-Hochberg adjustment. (I)
Kaplan-Meier curves, illustrating the survival of patients with
liver hepatocellular carcinoma based on Macro-c5-IFITM3
infiltration levels. The P-value was calculated using the log-rank
test. (J) Heatmap, showing normalized TF regulon activity in
macrophage and DC subtypes, as predicted by pySCENIC. DCs,
dendritic cells; UMAP, Unsupervised Uniform Manifold Approximation
and Projection; HBV, hepatitis B virus; DEGs, differentially
expressed genes; TF, transcription factor.

Figure 3

Single-cell RNA sequencing analysis
of primary HCC and adjacent non-tumor liver tissues. (A) A UMAP
plot displaying the distribution of cells from HCC and adjacent
tissues in six patients with HCC. (B) A dot plot illustrating the
percentages of cells expressing canonical marker genes alongside
their average expression levels across major cell types. (C) UMAP
plots showing the expression patterns of signature genes for six
major cell types, where different colors have been used to indicate
the expression levels. (D) Stacked bar plots representing the
proportions of major cell types in each sample, arranged by tissue
type. T, tumor; P, para-tumor. (E) Quantification of cell type
percentages in tumor and adjacent tissues is demonstrated (n=6
tissues from three HBV-positive and three HBV-negative patients;
mean ± SD). Statistical significance was calculated using unpaired
two-tailed Student's t-test. *P<0.05 and
****P<0.0001. UMAP, Unsupervised Uniform Manifold
Approximation and Projection; HCC, hepatocellular carcinoma; HBV,
hepatitis B virus; ns, no significance.

Figure 4

Single-cell RNA sequencing profiling
of HBV-infected and non-HBV-infected tissues with hepatocellular
carcinoma. (A) Density UMAP plots, illustrating the cell density of
clusters in HBV-negative (non-HBV-infected) and HBV-positive
(HBV-infected) samples. (B) Stacked bar plots, displaying the
percentages of major cell types in HBV-negative and HBV-positive
samples. (C) Summarized stacked bar plots, showing the overall
percentages of major cell types in HBV-negative and HBV-positive
samples. (D) UMAP plots, depicting further clustering of immune
cells in HBV-negative and HBV-positive samples, respectively. (E)
Quantification of the percentages of selected major immune cell
types in HBV-negative and HBV-positive samples is shown. (F)
Volcano plot, showing DEGs by comparing immune cells from
HBV-negative and HBV-positive samples. The P values were calculated
using the two-sided Wilcoxon rank-sum test, with Benjamini-Hochberg
adjustment. (G) Violin plots illustrating SLC35F1 expression
across immune cell types with or without HBV infection. Statistical
significance was calculated using unpaired two-tailed Student's
t-test. ****P<0.0001. (H) Dot plot, demonstrating the
top 5 DEGs for each immune cell type under HBV-negative and
HBV-positive conditions. (I) Volcano plot displaying overall DEGs
through combining all immune cells for a comparative analysis. The
P-values were generated using the two-sided Wilcoxon rank-sum test,
with Benjamini-Hochberg adjustment. (J) Gene set enrichment
analysis plot, indicating activated and suppressed Gene Ontology
terms in immune cells with or without HBV infection. The P-values
were calculated using the hypergeometric test, with
Benjamini-Hochberg adjustment. UMAP, Unsupervised Uniform Manifold
Approximation and Projection; HBV, hepatitis B virus; DEGs,
differentially expressed genes; DC, dendritic cells; NK, natural
killer cells.

Figure 5

Impact of HBV infection on
intercellular communication within HCC. (A) Bar plots comparing the
number of cellular interactions (left panel) and their interaction
strength (right panel) between HBV-Pos. and HBV-Neg. HCC samples.
(B) Dot plots illustrating the strength of outgoing and incoming
cellular communications for each cell type in HBV-Pos. (right) and
HBV-Neg. (left) samples. Each cell type is represented by a
specific color, and the size of the dot reflects the number of
cellular interactions. (C) Stacked bar plot, depicting the
information flow between the HBV-Neg. and HBV-Pos. conditions. (D)
Dot plot, highlighting upregulated checkpoint signaling pathways in
HBV-Pos. samples. HBV, hepatitis B virus; HCC, hepatocellular
carcinoma; HBV-Pos., HBV-infected; HBV-Neg; non-HBV-infected.
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Copy and paste a formatted citation
Spandidos Publications style
Liu K, Chen E, Liang J, Li Y, Cheng B, Shi W, Zhou Z, Zhou W, Tian H, Yang D, Yang D, et al: Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases. Mol Clin Oncol 23: 76, 2025.
APA
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W. ... Li, H. (2025). Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases. Molecular and Clinical Oncology, 23, 76. https://doi.org/10.3892/mco.2025.2871
MLA
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W., Zhou, Z., Zhou, W., Tian, H., Yang, D., Shao, X., Li, H."Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases". Molecular and Clinical Oncology 23.3 (2025): 76.
Chicago
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W., Zhou, Z., Zhou, W., Tian, H., Yang, D., Shao, X., Li, H."Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases". Molecular and Clinical Oncology 23, no. 3 (2025): 76. https://doi.org/10.3892/mco.2025.2871
Copy and paste a formatted citation
x
Spandidos Publications style
Liu K, Chen E, Liang J, Li Y, Cheng B, Shi W, Zhou Z, Zhou W, Tian H, Yang D, Yang D, et al: Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases. Mol Clin Oncol 23: 76, 2025.
APA
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W. ... Li, H. (2025). Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases. Molecular and Clinical Oncology, 23, 76. https://doi.org/10.3892/mco.2025.2871
MLA
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W., Zhou, Z., Zhou, W., Tian, H., Yang, D., Shao, X., Li, H."Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases". Molecular and Clinical Oncology 23.3 (2025): 76.
Chicago
Liu, K., Chen, E., Liang, J., Li, Y., Cheng, B., Shi, W., Zhou, Z., Zhou, W., Tian, H., Yang, D., Shao, X., Li, H."Single‑cell analysis reveals distinct immune characteristics of hepatocellular carcinoma in HBV‑positive vs. HBV‑negative cases". Molecular and Clinical Oncology 23, no. 3 (2025): 76. https://doi.org/10.3892/mco.2025.2871
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