A risk scoring model based on M2 macrophage-related genes for predicting prognosis of HBV-related hepatocellular carcinoma

doi:10.12122/j.issn.1673-4254.2024.05.04

Abstract

Abstract:

Objective To investigate the prognostic value of M2 macrophage-related genes (MRG) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Methods The transcriptome data of 73 patients with HBV-related HCC were obtained from TCGA database, and the MRG modules were identified by WGCNA. The MRG-based risk scoring model was constructed by LASSO regression analysis and validated using an external dataset. The correlation of the risk score with immune cell infiltration and drug sensitivity of HCC were analyzed with CIBERSORT and R.pRRophetic. The signaling pathways of the differential genes between the high- and low-risk groups were investigated using GSVA and GSEA enrichment analyses, and MRG expressions at the single cell level were validated using R.Seurat. The cell interaction intensity was analyzed by R.Cellchat to identify important cell types related to HCC progression. MRG expression levels were detected by RT-qPCR in THP-1 cells with HCC-conditioned medium-induced M2 polarization and in HBV-positive HCC cells. Results A high M2 macrophage infiltration level was significantly correlated with a poor prognosis of HCC, and 5 hub MRG (VTN, GCLC, PARVB, TRIM27, and GMPR) were identified. The overall survival of HCC patients was significantly lower in the high-risk than in the low-risk group. The high- and the low-risk groups showed significant enrichment of M2 macrophages and naïve B cells, respectively, and were sensitive to BI.2536 and to AG.014699, AKT.inhibitor.VIII, AZD.0530, AZD7762, and BMS.708163, respectively. The proliferation-related and metabolism-related pathways were enriched in the high-risk group, where monocytes showed the most active cell interactions during HCC progression. VTN was significantly upregulated in HCC cell lines, while GCLC, PARVB, TRIM27, and GMPR were upregulated in M2 THP-1 cells. Conclusion The MRG-based risk scoring model can accurately predict the prognosis of HBV-related HCC and reveal the differences in tumor microenvironment to guide precision treatment of the patients.

Key words: M2 macrophages, hepatocellular carcinoma, prognostic model, hepatitis B virus

Pengcheng LIU, Lijuan LOU, Xia LIU, Jian WANG, Ying JIANG. A risk scoring model based on M2 macrophage-related genes for predicting prognosis of HBV-related hepatocellular carcinoma[J]. Journal of Southern Medical University, 2024, 44(5): 827-840.

Figures/Tables 11

Tab.1 Baseline characteristics of HCC patients in TCGA dataset and GEO dataset

Item	Overall	GSE10140	TCGA-LIHC
n	155	82	73
Futime (median [IQR])	1613.00 [630.50, 2871.75]	2854.00 [1368.00, 3662.50]	682.50 [437.00, 1632.50]
Fustat =1 [n (%)]	48 (30.9)	32 (39.0)	16 (21.9)
EGF [n (%)]
High	9 (5.8)	9 (11.0)	0 (0.0)
Low	73 (47.1)	73 (89.0)	0 (0.0)
NA	73 (47.1)	0 (0.0)	73 (100.0)
Age (year, median [IQR])	52.00 [46.00, 62.75]	NA [NA, NA]	52.00 [46.00, 62.75]
Gender [n (%)]
Female	15 (9.7)	0 (0.0)	15 (20.5)
Male	58 (37.4)	0 (0.0)	58 (79.5)
NA	82 (52.9)	82 (100.0)	0 (0.0)
Grade (%)
G1	6 (3.8)	0 (0.0)	6 (8.2)
G2	27 (17.4)	0 (0.0)	27 (37.0)
G3	34 (21.9)	0 (0.0)	34 (46.6)
G4	6 (3.9)	0 (0.0)	6 (8.2)
NA	82 (52.9)	82 (100.0)	0 (0.0)
Stage [n (%)]
Stage I	49 (31.6)	0 (0.0)	49 (67.1)
Stage II	15 (9.8)	0 (0.0)	15 (20.5)
Stage III	5 (3.2)	0 (0.0)	5 (6.8)
Stage IV	1 (0.6)	0 (0.0)	1 (1.4)
Unknow	3 (1.8)	0 (0.0)	3 (4.2)
NA	82 (52.9)	82 (100.0)	0
T stage [n (%)]
T1	52(33.5)	0 (0.0)	52 (71.3)
T2	15 (9.8)	0 (0.0)	15 (20.5)
T3	5 (3.2)	0 (0.0)	5 (6.8)
T4	1 (0.6)	0 (0.0)	1 (1.4)
NA	82 (52.9)	82 (100.0)	0 (0.0)
Metastasis [n (%)]
M0	70 (45.3)	0 (0.0)	70 (95.8)
M1	2 (1.2)	0 (0.0)	2 (2.8)
MX	1 (0.6)	0 (0.0)	1 (1.4)
NA	82 (52.9)	82 (100.0)	0 (0.0)
Nodes [n (%)]
N0	70 (45.3)	0 (0.0)	70 (95.8)
NX	3 (1.8)	0 (0.0)	3 (4.2)
NA	82 (52.9)	82 (100.0)	0 (0.0)

Tab.2 RT-qPCR primer sequences

MRG	Forword primer (5'-3')	Reverse primer (5'-3')
GAPDH	AGATCCCTCCAAAATCAAGTGG	GGCAGAGATGATGACCCTTTT
VTN	AAGCCCCAAGTGACTCGC	TTTTCTCCTCGCCATCGTCA
GCLC	ACTTCATTTCCCAGTACCTTAACA	GCAGCACTCAAAGCCATAACA
GMPR	GGGCCACATCATCTCTGATGGA	TCAGTCCCCCGAGAATATCCAG
PARVB	GGTTCACTTCTCCCTGGCTC	CGCTCCTCGTTCTCCTCAAG
TRIM27	AGCATGAGTATCGCCTCCTG	CTGATTCTTTCAGCCCTGCTC

Fig.1 HCC patients with high M2 macrophage infiltration level have poor prognosis. A: Proportion of immune cells in HBV-related HCC patients. B-D: Prognosis of patients stratified by the number of M0 macrophage (B), M1 macrophage (C) and M2 macrophage (D).

Fig.2 Construction of prognostic risk scoring model. A-C: Identification of M2 macrophage-related gene (MRG) modules by WGCNA. D-F: Five hub MRGs (VTN, GCLC, PARVB, TRIM27 and GMPR) identified by LASSO regression analysis for constructing the risk scoring model.

Fig.3 Validation of the risk scoring model. A-D: Different patterns of survival status and survival time between the high-risk group (A, B) and low-risk group (C, D). E, F: Validation of the MRG prognostic model in the training dataset (E) and testing dataset (F). G, H: ROC curves of the risk score for predicting overall survival (OS) in the training dataset (G) and testing dataset (H). I: Validation of the MRG prognostic model using the external dataset.

Fig.4 Construction of the nomogram. A: Nomogram of the risk scores and clinical characteristics. B: Calibration curves for evaluating OS predictions at 3 and 5 years. C, D: ROC and DCA curves for determining the accuracy of the nomogram for OS at 1, 3 and 5 years, respectively. E-G: Correlation of the risk scores with clinical stages. H, I: Univariate analysis and multivariate analysis for validating the independent prognostic value of the risk scores.

Fig.5 Clinical predictive value of the risk scoring model. A, B: Infiltrating level of immune cells in the high- and low-risk groups. C: Sensitivity of anti-tumor immunotherapy in the high- and low-risk groups. D: IC50 values of common chemotherapy drugs.

Fig.6 Functional enrichment analysis of signaling pathways related to the risk scoring model. A: GSVA in the high- and low-risk groups. B: GSEA in the high- and low-risk groups. C: Molecular interaction networks between the pathways.

Fig.7 Relationship between the 5 hub MRGs and HCC pathogenic genes. A: Relationship between the hub genes and the top 20 HCC pathogenic genes. B: A bubble plot illustrating the Pearson correlation between 5 hub MRGs (VTN, GCLC, PARVB, TRIM27 and GMPR) and the top 20 HCC pathogenic genes.

Fig.8 Expression of MRGs in different cell types. A-C: 5 hub MRGs are mainly expressed in hepatocytes, monocyte, T cells and NK cell. D, E: Cell chat intensity in high- and low-risk groups. F: UMAP projection showing the immune landscape of HCC, colored by cluster. G: Expressions of GCLC, PARVB, GMPR and TRIM27 in HCC immune microenvironment.

Fig.9 Expressions of MRGs in HCC cell lines and macrophages. A: Expression of CD163 and CD206 on THP-1 cells detected by flow cytometry. B-F: Expression of MRGs in PLC/PRF/5 and HCM-THP-1 cells. **P<0.01, ***P<0.001, ****P<0.0001.

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