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Article Open Access

Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma

  • Authors:
    • Aiping Wang
    • Xiaojing Li
    • Zi Wang
    • Lan Chen
  • View Affiliations / Copyright

    Affiliations: Department of Gastroenterology, The Sixth Hospital of Wuhan, Affiliated Hospital of Jianghan University, Wuhan, Hubei 430015, P.R. China, Department of Pathology, Wuhan Puren Hospital, Puren Hospital Affiliated of Wuhan University of Science and Technology, Wuhan, Hubei 430080, P.R. China, College of Medicine, Hubei Three Gorges Polytechnic, Yichang, Hubei 443000, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 540
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    Published online on: September 22, 2025
       https://doi.org/10.3892/ol.2025.15286
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Abstract

Liver hepatocellular carcinoma (LIHC) is a major contributor to cancer‑associated mortality worldwide, with a poor prognosis due to late‑stage diagnosis and limited therapeutic options. M2 macrophages serve crucial roles in the tumor microenvironment (TME), contributing to tumor progression, immune evasion and resistance to therapy. However, the prognostic importance of M2 macrophage‑related genes in LIHC and their potential for predicting responses to immunotherapy remain underexplored. A bioinformatics analysis was performed using The Cancer Genome Atlas data to identify M2 macrophage‑related genes in LIHC. Differential expression and weighted gene co‑expression network analysis were used to identify key gene modules and a prognostic model was developed and validated using Kaplan‑Meier analysis and receiver operating characteristic curves. The immune therapy response was assessed using tracking of indels by decomposition and Submap analyses. Killer cell lectin like receptor B1 (KLRB1) was knocked down in HuH‑7 cells and overexpressed in HuH‑1 cells to evaluate its effect on tumor cells and macrophage regulation. The effect on human umbilical vein endothelial cell tube formation was also assessed. A total of two M2 macrophage‑related genes (KLRB1 and phosphatidylinositol‑5‑phosphate 4‑kinase type 2α) were identified as notable prognostic biomarkers for LIHC. The prognostic model demonstrated certain predictive power, with high‑risk patients exhibiting markedly worse overall survival. This model was validated in external datasets and associated with immune infiltration patterns. Furthermore, low‑risk patients were more likely to respond to immune checkpoint blockade therapy. The inhibition of KLRB1 enhanced LIHC cell activity and increased macrophage polarization from the M0 phenotype to the M2 phenotype by regulating LIHC cell secretions. In conclusion, M2 macrophage‑related genes are valuable prognostic biomarkers in LIHC. The prognostic model effectively stratifies patients by survival risk and predicts immunotherapy responses, thereby highlighting the potential for improved TME‑targeted therapies in LIHC. The mechanism of KLRB1 regulation in LIHC‑macrophage interactions and its impact on LIHC activities were also evaluated.
View Figures

Figure 1

Macrophage infiltration and M2
macrophage differential expression in LIHC. (A) Heatmap
illustrating macrophage infiltration levels in LIHC and normal
tissue samples, as quantified by six different immune infiltration
analysis methods. The heatmap provides a visual representation of
macrophage levels across all methods. The red lines indicate the
distribution of infiltrating macrophages in both LIHC and normal
samples across the different methods. (B) Differential expression
analysis of M2 macrophages between patients with LIHC and normal
controls. Significant differences in M2 macrophage expression were
observed in LIHC samples compared with normal controls across all
methods, with the exception of the MCPCOUNTER method. LIHC, liver
hepatocellular carcinoma.

Figure 2

Identification and preservation of
gene modules associated with M2 macrophages. (A) Determination of
the optimal soft threshold for network construction based on the
scale-free topology model fit. A power value of 14 was selected,
achieving a model fit close to 0.9. (B) Dendrogram showing the
identification of 16 gene modules using weighted gene co-expression
network analysis. (C) Preservation analysis of the identified gene
modules. Modules with Z scores <2 (grey, gold, midnight blue and
black) were excluded due to poor preservation. (D) Correlation
analysis between M2 macrophage expression levels and the gene
modules. ME, module eigengene.

Figure 3

Development and validation of the M2
macrophage-related prognostic model. (A) Univariate Cox regression
analysis identified 14 genes with significant prognostic value
(P<0.05) in patients with LIHC. (B) Least Absolute Shrinkage and
Selection Operator-Cox regression was applied to refine the gene
set, selecting three key genes based on a λ value of 0.0514
(10-fold cross-validation). (C) Multivariate Cox regression
confirmed the independent prognostic significance of the two
selected genes. A prognostic risk score model was constructed using
the following formula: RiskScore = PIP4K2A × 1.0165 + KLRB1 ×
0.8897. (D) Kaplan-Meier survival curves comparing two risk groups
in the TCGA-LIHC cohort. A significant difference in overall
survival was observed between the groups. (E) Time-dependent
receiver operating characteristic curve analysis of the prognostic
risk score model, showing certain predictive performance across
three independent cohorts: TCGA-LIHC, GSE76427 (Gene Expression
Omnibus) and LIRI-JP (International Cancer Genome Consortium).
LIHC, liver hepatocellular carcinoma; TCGA, The Cancer Genome
Atlas; PIP4K2A, phosphatidylinositol-5-phosphate 4-kinase type 2α;
SH3BP1, SH3 domain binding protein 1; KLRB1, killer cell lectin
like receptor B1; AUC, area under the curve; TMSB4XP8, TMSB4X
pseudogene 8; STK10, serine/threonine kinase 10; PTGER4,
prostaglandin E receptor 4; PLEKHA2, pleckstrin homology domain
containing A2; MX2, MX dynamin like GTPase 2; MCUB, mitochondrial
calcium uniporter dominant negative subunit β; IL2RG, IL2 receptor
subunit γ; GMIP, GEM interacting protein; EAF2, ELL associated
factor 2; ADAM19, ADAM metallopeptidase domain 19; HR, hazard
ratio.

Figure 4

Differential pathway and functional
enrichment analysis. (A) GSVA of the hallmark gene sets revealed
significant differences in pathway activation between the two risk
groups. (B) GO functional enrichment analysis identified
significant enrichment for pathways related to cellular polarity
and apical membrane organization in the high-risk group. (C) GO
analysis of the low-risk group showed enrichment for immune
response-related pathways, including immunoglobulin antigen
binding. (D) KEGG pathway enrichment analysis identified
differential activation of signaling pathways between the two risk
groups. Pathways were categorized based on shared biological
functions to reveal common themes across the groups. GO, Gene
Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSVA, Gene
Set Variation Analysis; BP, biological process; CC, cellular
component; MF, molecular function.

Figure 5

Immune infiltration, TMB, immune
checkpoint expression and immunotherapy response in two risk
groups. (A) Correlation between the prognostic risk score and the
immune, stromal and ESTIMATE scores, calculated using the ESTIMATE
algorithm. (B) Correlation analysis between the risk score and TMB
showed no significant association. (C) Differential expression
analysis of inhibitory immune checkpoint genes revealed significant
differences between two risk groups. (D) Differential expression of
stimulatory immune checkpoint genes showed no significant
differences between the two groups. (E) TIDE analysis demonstrated
that low-risk patients had significantly lower TIDE scores,
suggesting they have an improved immune response and a reduced
potential for immune escape. (F) SubMap analysis predicted that
low-risk patients are more likely to respond to immune checkpoint
blockade therapy, highlighting their greater potential for
benefiting from immunotherapy. PD1-R indicates a response to PD1
treatment, while PD1-noR indicates no response to PD1 treatment.
CTLA4-R indicates a response to CTLA4 treatment, while CTLA4-noR
indicates no response to CTLA4 treatment. *P<0.05; **P<0.01;
***P<0.001; ****P<0.0001; t-test based P-value. TMB, tumor
mutational burden; TIDE, Tumor Immune Dysfunction and Exclusion;
PD1, programmed cell death protein 1; CTLA4, cytotoxic T-lymphocyte
associated protein 4.

Figure 6

KLRB1 expression and ligand-receptor
interactions in the TME. (A) Expression of KLRB1 across six
single-cell RNA-sequencing datasets from the Tumor Immune Single
Cell Hub 2 database. KLRB1 was predominantly expressed in
CD8+ T cells, CD8+ Tex and NK cells. (B)
Heatmap showing the strength of ligand-receptor interactions
between different immune cell populations. (C) Network diagram
illustrating the intensity of ligand-receptor interactions between
CD8+ T cells and other cell populations in the TME.
KLRB1, killer cell lectin like receptor B1; TME, tumor
microenvironment; NK, natural killer; Tex, exhausted T cells; LIHC,
liver hepatocellular carcinoma; DC, dendritic cell; Treg,
T-regulatory cell.

Figure 7

Pan-cancer analysis of KLRB1
expression and prognostic impact. (A) Expression of KLRB1 across
multiple cancer types in The Cancer Genome Atlas. Significant
differences in KLRB1 expression were observed in several cancer
types, including LIHC. (B) Prognostic analysis of KLRB1 expression
in pan-cancer datasets, including DFI, DSS, OS and PFI. (C) Forest
plot analysis of OS based on KLRB1 expression in pan-cancer. (D)
Gene Set Enrichment Analysis revealed significant biological
pathways enriched in both high- and low-expression groups,
suggesting that KLRB1 may regulate important tumor-related
processes. *P<0.05; **P<0.01; ***P<0.001; t-test based
P-value. KLRB1, killer cell lectin like receptor B1; LIHC, liver
hepatocellular carcinoma; DFI, disease-free interval; DSS,
disease-specific survival; OS, overall survival; PFI, platinum-free
interval; FDR, false discovery rate; NES, normalized enrichment
score; ACC, adrenocortical carcinoma; BLCA, bladder cancer; BRCA,
breast invasive carcinoma; CESC, cervical squamous cell carcinoma;
CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, diffuse
large B-cell lymphoma; ESCA, esophageal carcinoma; GBM,
glioblastoma multiforme; HNSC, head and neck squamous cell
carcinoma; KICH, kidney chromophobe; KIRC, kidney clear cell
carcinoma; KIRP, kidney renal papillary cell carcinoma; LGG, brain
lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD,
lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO,
mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD,
pancreatic adenocarcinoma; PCPG, pheochromocytoma and
paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum
adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD,
stomach adenocarcinoma; TGCT, testicular germ cell tumor; THCA,
thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrioid
carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

Figure 8

KLRB1 regulates the activity of LIHC
cells and its influence on macrophages through LIHC cells. (A) qPCR
detection of KLRB1 expression in HuH-7 and HuH-1 cells. (B)
Validation of shRNA knockdown KLRB1 in the HuH-7 cell line and
lentivirus overexpression of KLRB1 in the HuH-1 cell line. (C) Cell
proliferation curve of sh-KLRB1-2 and OE-KLRB1. Cell migration
assay of (D) sh-KLRB1-2 and (E) OE-KLRB1 cells (scale bars, 100
µm). (F) Annexin V-FITC/PI assessed apoptosis in sh-KLRB1-2 and
OE-KLRB1 transfected cells. (G) Tube formation assay of HUVECs
treated with sh-KLRB1-2 and OE-KLRB1 cell conditioned media (scale
bar, 200 µm). (H) qPCR detection of polarization markers in M0 to
M1/M2 cells, treated with sh-KLRB1-2 or OE-KLRB1 cell conditioned
media. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001;
t-test based P-value. LIHC, liver hepatocellular carcinoma; PI,
propidium iodide qPCR, quantitative PCR; KLRB1, killer cell lectin
like receptor B1; sh, short hairpin; OE, overexpression; NC,
negative control; OD, optical density.
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Copy and paste a formatted citation
Spandidos Publications style
Wang A, Li X, Wang Z and Chen L: Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma. Oncol Lett 30: 540, 2025.
APA
Wang, A., Li, X., Wang, Z., & Chen, L. (2025). Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma. Oncology Letters, 30, 540. https://doi.org/10.3892/ol.2025.15286
MLA
Wang, A., Li, X., Wang, Z., Chen, L."Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma". Oncology Letters 30.6 (2025): 540.
Chicago
Wang, A., Li, X., Wang, Z., Chen, L."Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma". Oncology Letters 30, no. 6 (2025): 540. https://doi.org/10.3892/ol.2025.15286
Copy and paste a formatted citation
x
Spandidos Publications style
Wang A, Li X, Wang Z and Chen L: Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma. Oncol Lett 30: 540, 2025.
APA
Wang, A., Li, X., Wang, Z., & Chen, L. (2025). Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma. Oncology Letters, 30, 540. https://doi.org/10.3892/ol.2025.15286
MLA
Wang, A., Li, X., Wang, Z., Chen, L."Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma". Oncology Letters 30.6 (2025): 540.
Chicago
Wang, A., Li, X., Wang, Z., Chen, L."Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma". Oncology Letters 30, no. 6 (2025): 540. https://doi.org/10.3892/ol.2025.15286
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