M2巨噬细胞特征基因风险评分能准确预测HBV相关肝细胞癌患者的预后

doi:10.12122/j.issn.1673-4254.2024.05.04

摘要/Abstract

摘要：

目的探讨在乙型肝炎病毒（HBV）相关肝细胞癌（HCC）中M2巨噬细胞特征基因（MRG）对患者预后的评估价值及潜在的分子机制。方法从TCGA数据库获取73例HBV相关肝HCC患者的转录组数据，通过WGCNA识别M2巨噬细胞相关基因模块，利用LASSO鉴定出关键MRG并构建风险评分，并在外部数据集中验证风险评分的预测性能。应用CIBERSORT和R.pRRophetic分析风险评分与免疫细胞浸润、药物敏感性的关系。通过GSVA和GSEA对高风险组和低风险组的差异基因进行通路富集分析。R.Seurat验证在HCC中表达MRG的细胞类型，并通过R.Cellchat分析细胞互作强度，找到与HCC进展相关的重要细胞类型。流式细胞术检测肝癌条件培养基诱导THP-1向M2样极化，RT-qPCR验证MRG在HBV阳性的肝癌细胞系和M2巨噬细胞中表达。结果 M2巨噬细胞高浸润状态与患者不良预后显著相关（P=0.025）。高风险组的总生存期（OS）均显著低于低风险组（训练集P=0.021，测试集P=0.046）。高风险组中M2巨噬细胞显著富集（P=0.03），低风险组中幼稚B细胞显著富集（P=0.049）。药物BI.2536对高风险组更有效（P=0.025），AG.014699（P=0.044）、AKT.inhibitor.VIII（P=0.041）、AZD.0530（P=0.0033）、AZD7762（P=0.0051）和BMS.708163（P=0.015）对低风险组更有效。通路富集分析结果表明，增殖相关通路和代谢相关通路在高风险组中富集。单核细胞在高风险组HCC进展的细胞互作中最为活跃。VTN在PLC/PRF/5中的表达显著上调（P<0.0001），GCLC、PARVB、TRIM27和GMPR在M2样THP-1中的表达显著上调（P=0.0037、P=0.0015、P=0.0071、P=0.0004）。结论 MRG风险评分能准确预测HBV相关HCC患者的预后，揭示其肿瘤微环境的差异，为HCC患者的精准治疗提供了指导。

关键词: M2巨噬细胞, 肝细胞癌, 预后模型, 乙型肝炎病毒

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

刘鹏程, 娄丽娟, 刘霞, 王建, 姜颖. M2巨噬细胞特征基因风险评分能准确预测HBV相关肝细胞癌患者的预后[J]. 南方医科大学学报, 2024, 44(5): 827-840.

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.

图/表 11

表1 TCGA数据集和GSE10140数据集患者的基线资料

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)

表2 MRG基因的RT-qPCR引物序列

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

图1 M2巨噬细胞高浸润的HCC患者预后不良

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).

图2 风险评分的构建

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.

图3 风险评分的验证

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.

图4 列线图的构建

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.

图5 风险评分的临床预测价值

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.

图6 风险评分相关的功能富集分析

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.

图7 MRG与HCC致病基因的关系

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.

图8 表达MRG的细胞类型

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.

图9 MRG在肝癌细胞和巨噬细胞中的表达

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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