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. 2025 Jan 16;16:54. doi: 10.1007/s12672-025-01785-z

Mutual causal effects between immune cells and hepatocellular carcinoma: a Mendelian randomization study

Zheng Wang 1, Mengshu Pan 2, Jie Zhu 3,✉, Changhong Liu 4,✉
PMCID: PMC11739439  PMID: 39820843

Abstract

Background

Hepatocellular carcinoma (HCC), a malignant tumor that seriously endangering health, has aroused widespread concern in the field of public health. Previous researches have noted the relationships between immune cells and HCC, but the causal relationship was uncertain.

Methods

In this study, a bidirectional two sample Mendelian randomization (MR) analysis was utilized to access the causal relationship between immune cell characteristics and HCC. According to the open-access data, we investigated the causal relationship between 731 immune cell characteristics and HCC risk.

Results

After screening by IVW approach, increased levels of 8 immune traits and reduced levels of 7 immune traits could lead to changes in HCC risk. These 15 immune cells were distributed in the Monocyte (4 cells), Treg panel (4 cells), TBNK (3 cells), Maturation stages of T cell panel (3 cells), and cDC panel (1 cells). Furthermore, HCC was identified to have causal effects on 21 immunophenotypes. Among these immune cells, hepatocarcinogenesis had the greatest impact on CD4 on EM CD4 + and CD33 on Mo MDSC.

Conclusions

This study enhances our comprehension of the interaction between immune cells and HCC risk, furnishing novel avenues to explore the mechanisms of HCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-01785-z.

Keywords: Immune cell, Hepatocellular carcinoma, Mendelian randomization, Immunophenotypes

Introduction

Hepatocellular carcinoma (HCC) is an aggressive malignancy that remains one of the most common causes of cancer-relevant death worldwide [1]. Although the therapeutic measures, including surgical resection, have been improved, the 5-year survival rate for HCC patients is still disappointing [2]. HCC is a multifactorial illness with numerous underlying causes [3]. Genetic and immune environmental factors participate in its occurrence and progress, yet the specific mechanisms remain unclear.

The liver, as a central immunological organ, has a large number of immune cells that are exposed to various microbial antigens via the gut-liver axis [4]. Moreover, the immune cell composition of HCC tumor microenvironment possesses a significant influence on tumor biology [5]. Many studies have confirmed a bidirectional relationship between HCC and immune cells. NK cells are the main components of innate immune cells in the liver, playing a core role in innate immunity [6]. NK cells play a double-edged role in chronic HBV infection [7]. NK cells possess anti-fibrosis function [8], while NK cell activation mediates HBV-related liver cell injury, thereby promoting HCC development [9]. Dendritic cells (DCs) are specialized antigen-presenting cells that play a prominent role in starting immune responses [10]. DCs play a complex role in the occurrence and development of HCC [11]. On the one hand, DCs could fight against HCC by activating T cell responses [12]. On the other hand, regulatory DCs could inhibit anti-tumor immunity and promote tumor immune escape through indoleamine-2,3-dioxygenase [13]. Nevertheless, most of the existing evidence was according to observational studies, which may have restrictions owing to confounding variables and reverse causation.

The causal associations between immune cell characteristics and HCC have not been well elucidated.

Mendelian randomization (MR) is an emerging analytical approach utilized for causal inference of risk factors and disorders in epidemiology [14]. Single nucleotide polymorphisms (SNPs) were utilized as an instrumental variable to unveil the causality of the correlation between exposure and outcome. SNPs follow the criterion of random genetic variation distribution during meiosis, eliminating the effect of mixed factors and the underlying effect of reverse causality [15]. Former researches have illustrated that there were plenty of correlations between immune cell characteristics and various diseases, confirming the hypothesis of associations among them [16, 17]. In this research, a comprehensive two-sample MR approach was conducted to investigate the causal relationship between 731 immune cell characteristics and HCC.

Materials and methods

Study design

According to the massive amounts of GWAS data, we evaluated the bi-directional causal relationships between a great quantity of immune traits (731 immunophenotypes in seven groups [18]) and hepatocellular carcinoma risk via a two-sample Mendelian randomization method. To guarantee effective instrumental variable estimation in MR analyses, three principal assumptions must be satisfied [19]. First, the selected SNPs should be intimately and strongly connected to exposure factors. Second, the SNPs ought to be independent of any potential confounding elements. Last but not least, the SNPs only influences the outcome via exposure variables and are not affected by other ways.

Data sources

We selected the SNPs related to hepatocellular carcinoma (HCC) from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/datasets/bbj-a-158/), which conducted a genome-wide association study (GWAS) on 197,611 East Asian individuals (Ncase = 1866, Ncontrol = 195,745). The dataset included approximately 8,885,115 SNPs. This study was chosen due to its large sample size, specifically for East Asian populations, and its focus on hepatocellular carcinoma, making it highly relevant for our analysis of genetic risk factors for HCC. The selection of this dataset ensures that our analysis is based on a well-powered GWAS with a large control group, minimizing the risk of confounding bias [20].

Additionally, we extracted the GWAS summary statistics for 731 immune cell phenotypes from the GWAS Catalog (accession numbers from GCST90001391 to GCST90002121). These 731 immunophenotypes were classified into four broad categories: 389 median fluorescence intensities (MFI), 192 relative cell counts (RC), 118 absolute cell counts (AC), and 32 morphological parameters (MP). We chose this dataset because it provides detailed immune cell measurements that are crucial for understanding the immune landscape in relation to HCC risk. These phenotypes are comprehensive, encompassing both quantitative and morphological immune traits, which are essential for capturing the immune profile associated with hepatocellular carcinoma.

For the association analysis, approximately 22 million high-quality markers were retained after quality control. We performed the analysis by adjusting for gender, age, and age2 as covariates, to account for potential confounding effects and improve the precision of our findings [21].

Instrument selection

Given a quantity of single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 1 × 10−5) for 731 immunophenotypes, we adopted a more stringent correlation threshold (p < 5 × 10−8) as the inclusion criteria. SNPs (r2 > 0.001 within 10,000 kilobases) with palindromic structure were excluded. SNPs with F statistics fewer than 10 were identified as weak tools and also excluded (Table 1). In order to assess causal link between 731 immune traits and HCC, inverse variance weighting (IVW) [22], MR Egger and Weighted median were chiefly implemented via the ‘TwoSampleMR’ package (version 0.5.7). The Q statistic of Cochran and the homologous p values were utilized to measure the heterogeneity between the chosen IVs. In order to avoid the instrumental variables affecting the outcome through other factors than the exposure factors, we analyzed the pleiotropy of the instrumental variables via MR-Egger intercept. Only the absence of pleiotropy could indicate the robustness of the results. The results were represented by estimated values (odds ratio (OR) and its 95% confidence interval (CI)), and plotted using a forest map. All analyses were implemented via the TwoSampleMR package in R (version 4.3.1). A two-sided p < 0.05 was considered statistically significant.

Table 1.

Instrumental variables used in MR analysis of the association between immune cell traits and HCC risk

Exposure SNP Chr Position Exposure Outcome F-value
Cell type Characteristics Beta SE P-value Beta SE P-value
cDC CD11c on granulocyte rs113861918 19 52129097 − 0.215 0.037 6.42E-09 − 0.059 0.047 0.208 33.888
rs1801274 1 161479745 0.248 0.030 1.36E-16 0.098 0.041 0.017 431.123
rs28432694 16 48666310 − 0.172 0.030 1.75E-08 − 0.006 0.038 0.879 31.910
Maturation stages of T cell CD8 on EM CD8br rs17201560 6 32047268 − 0.519 0.086 2.13E-09 − 0.081 0.108 0.450 36.053
rs2571390 6 29923554 0.364 0.038 6.16E-22 0.089 0.054 0.096 94.109
rs3020726 2 87016506 − 0.298 0.040 7.15E-14 − 0.067 0.075 0.373 194.449
Naive CD4 + %CD4 +  rs113243185 6 32585071 − 0.210 0.036 8.06E-09 0.039 0.045 0.391 62.639
rs12712610 2 38897249 0.146 0.025 1.18E-08 − 0.069 0.034 0.043 68.111
rs9271536 6 32589978 0.232 0.033 3.60E-12 − 0.115 0.039 0.004 76.551
TD DN (CD4-CD8-) %T cell rs111983490 6 32205324 − 0.217 0.037 3.74E-09 0.008 0.044 0.846 62.119
rs6729180 2 38920621 0.202 0.026 1.78E-14 − 0.075 0.033 0.026 60.235
rs6755452 2 87035076 0.190 0.032 4.75E-09 − 0.050 0.075 0.506 51.878
Monocyte CD40 on CD14 + CD16- monocyte rs1801274 1 161479745 0.437 0.026 1.62E-62 0.099 0.041 0.017 431.123
rs60629714 9 95889160 0.186 0.032 4.55E-09 0.0006 0.040 0.988 34.513
rs745307 20 44747086 0.664 0.033 2.07E-83 0.024 0.088 0.787 458.015
CX3CR1 on CD14 + CD16- monocyte rs1801274 1 161479745 − 0.450 0.026 2.10E-64 0.098 0.041 0.017 431.123
rs72865957 3 39355919 0.283 0.029 6.00E-23 − 0.110 0.069 0.111 98.618
rs9823718 3 39293757 − 0.412 0.037 5.78E-29 0.104 0.059 0.081 126.907
CX3CR1 on CD14 + CD16 + monocyte rs1801274 1 161479745 − 0.463 0.026 2.62E-69 0.098 0.041 0.017 431.122
rs2649752 3 39351805 0.158 0.029 3.51E-08 − 0.111 0.069 0.108 30.499
rs9823718 3 39293757 − 0.327 0.037 5.15E-19 0.103 0.059 0.081 126.907
CX3CR1 on monocyte rs1801274 1 161479745 − 0.410 0.026 4.45E-54 0.099 0.041 0.017 431.123
rs72865957 3 39355919 0.239 0.028 6.28E-17 − 0.110 0.069 0.111 98.617
rs9823718 3 39293757 − 0.408 0.036 7.85E-29 0.104 0.059 0.081 126.907
TBNK FSC-A on CD4 +  rs13191523 6 31009094 0.277 0.046 1.73E-09 − 0.162 0.309 0.599 36.449
rs7745305 6 30913475 0.206 0.038 4.51E-08 0.072 0.218 0.739 30.063
rs9271673 6 32592833 − 0.209 0.033 1.93E-10 − 0.117 0.039 0.003 40.784
SSC-A on CD4 +  rs113243185 6 32585071 0.240 0.037 1.39E-10 0.038 0.045 0.391 62.639
rs148031710 6 31148873 0.297 0.049 1.91E-09 − 0.145 0.548 0.791 36.264
rs79493037 6 31405825 0.229 0.041 2.31E-08 0.081 0.066 0.220 31.354
rs9271536 6 32589978 − 0.259 0.035 8.85E-14 − 0.114 0.039 0.004 76.551
SSC-A on granulocyte rs2236947 3 50371432 0.181 0.028 2.24E-10 0.066 0.039 0.097 40.494
rs2738144 8 6826043 − 0.246 0.029 1.59E-17 − 0.016 0.057 0.772 73.405
rs42031 7 92237396 − 0.239 0.030 5.55E-15 − 0.067 0.081 0.407 61.636
rs445 7 92408370 0.437 0.032 3.84E-40 0.057 0.035 0.120 180.877
Treg CD28- CD8br %CD8br rs4796089 17 33814758 − 0.203 0.033 1.35E-09 0.085 0.038 0.027 43.052
rs6904670 6 31192797 − 0.146 0.026 3.34E-08 0.059 0.035 0.088 30.622
rs7728865 5 59819941 − 0.217 0.038 9.95E-09 − 1.051 1.667 0.528 33.011
CD39 on granulocyte rs11766311 7 77134132 − 0.153 0.028 4.55E-08 − 0.072 0.035 0.042 30.046
rs445 7 92408370 0.209 0.034 1.06E-09 0.057 0.035 0.110 180.877
rs7073937 10 97469109 − 0.204 0.034 2.45E-09 − 0.046 0.052 0.375 35.786
CD127 on CD45RA + CD4 +  rs11567754 5 35872190 − 0.298 0.043 8.08E-12 0.029 0.052 0.583 47.087
rs1573735 16 423696 − 0.207 0.028 4.02E-13 − 0.017 0.036 0.638 53.075
rs3129891 6 32415080 − 0.237 0.034 5.88E-12 0.102 0.035 0.004 47.719
rs9271772 6 32594275 0.261 0.035 2.23E-13 − 0.114 0.039 0.004 54.239
CD8 on CD39 + CD8br rs111359007 6 31773975 − 0.280 0.048 5.34E-09 0.960 1.415 0.498 34.238
rs117519014 10 97403248 0.435 0.066 7.57E-11 2.090 1.015 0.040 42.658
rs17197199 6 31138472 − 0.284 0.043 7.21E-11 − 0.031 0.042 0.455 42.742
rs72847706 2 87055981 − 0.255 0.032 1.80E-15 − 0.075 0.054 0.168 63.946
rs9357097 6 30285121 0.192 0.028 8.08E-12 0.065 0.033 0.046 47.099
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SNP single nucleotide polymorphism, Chr Chromosome, SE Standard Error

Results

Investigation of the causal impact of immune cells on HCC risk

In order to investigate the causal impact of immune cells on HCC risk, two-sample MR Analyses were carried out, and the IVW approach was occupied as the primary analysis. After screening by IVW approach, increased levels of 8 immune traits and reduced levels of 7 immune traits could lead to changes in HCC risk (Fig. 1). 15 immune cells were detected, including 4 in the Monocyte and Treg panel, 3 in the TBNK and Maturation stages of T cell panel, and 1 in the cDC panel (Table 2).

Fig. 1.

Fig. 1

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Forest plots showed the causal associations between immune cell traits and HCC by using IVW method

Table 2.

MR results for causal impact of immune cells on HCC risk

Exposure Method No SNP OR (95%CI) P-Value
Cell type Characteristics
cDC CD11c on granulocyte IVW 3 1.31 (1.05 to 1.63) 0.018
Weighted median 1.36 (1.03 to 1.79) 0.029
Weighted mode 1.42 (1.00 to 2.02) 0.192
Simple mode 1.38 (1.94 to 2.04) 0.244
MR Egger 3.39 (0.81 to 14.18) 0.343
Maturation stages of T cell CD8 on EM CD8br IVW 3 1.24 (1.00 to 1.54) 0.045
Weighted median 1.26 (1.00 to 1.59) 0.053
Weighted mode 1.27 (0.96 to 1.69) 0.234
Simple mode 1.27 (0.94 to 1.70) 0.260
MR Egger 1.06 (0.33 to 3.41) 0.935
Naive CD4 + %CD4 +  IVW 3 0.67 (0.54 to 0.84) 0.001
Weighted median 0.62 (0.47 to 0.83) 0.001
Weighted mode 0.61 (0.43 to 0.88) 0.119
Simple mode 0.62 (0.42 to 0.90) 0.132
MR Egger 0.73 (0.19 to 2.75) 0.722
TD DN (CD4-CD8-) %T cell IVW 3 0.79 (0.62 to 0.99) 0.049
Weighted median 0.75 (0.56 to 1.00) 0.046
Weighted mode 0.70 (0.49 to 0.99) 0.188
Simple mode 0.74 (0.49 to 1.12) 0.288
MR Egger 15.53 (0.05 to 5008.70) 0.522
Monocyte CD40 on CD14 + CD16- monocyte IVW 3 1.15 (1.00 to 1.33) 0.047
Weighted median 1.15 (0.98 to 1.33) 0.078
Weighted mode 1.25 (1.05 to 1.50) 0.135
Simple mode 1.02 (0.79 to 1.32) 0.894
MR Egger 1.21 (0.77 to 1.89) 0.559
CX3CR1 on CD14 + CD16- monocyte IVW 3 0.78 (0.68 to 0.91)  < 0.001
Weighted median 0.79 (0.68 to 0.93) 0.003
Weighted mode 0.80 (0.68 to 0.94) 0.115
Simple mode 0.79 (0.64 to 0.97) 0.158
MR Egger 1.07 (0.42 to 2.76) 0.909
CX3CR1 on CD14 + CD16 + monocyte IVW 3 0.78 (0.67 to 0.91) 0.002
Weighted median 0.79 (0.67 to 0.93) 0.004
Weighted mode 0.80 (0.67 to 0.94) 0.115
Simple mode 0.77 (0.61 to 0.97) 0.161
MR Egger 1.04 (0.63 to 1.73) 0.899
CX3CR1 on monocyte IVW 3 0.77 (0.66 to 0.90) 0.001
Weighted median 0.78 (0.66 to 0.93) 0.004
Weighted mode 0.78 (0.65 to 0.95) 0.126
Simple mode 0.78 (0.63 to 0.96) 0.148
MR Egger 1.06 (0.44 to 2.58) 0.917
TBNK FSC-A on CD4 +  IVW 3 1.68 (1.18 to 2.41) 0.005
Weighted median 1.72 (1.12 to 2.65) 0.014
Weighted mode 1.74 (1.15 to 2.63) 0.121
Simple mode 1.58 (0.65 to 3.82) 0.419
MR Egger 0.017 (2.05E-06 to 147.78) 0.541
SSC-A on CD4 +  IVW 4 1.39 (1.12 to 1.73) 0.002
Weighted median 1.45 (1.12 to 1.87) 0.004
Weighted mode 1.46 (1.12 to 1.90) 0.068
Simple mode 1.42 (1.04 to 1.93) 0.112
MR Egger 6.12 (0.077 to 487.12) 0.502
SSC-A on granulocyte IVW 4 1.17 (1.02 to 1.34) 0.029
Weighted median 1.15 (0.99 to 1.34) 0.073
Weighted mode 1.14 (0.96 to 1.34) 0.236
Simple mode 1.14 (0.90 to 1.44) 0.353
MR Egger 0.99 (0.67 to 1.47) 0.984
Treg CD28- CD8br %CD8br IVW 3 0.66 (0.50 to 0.89) 0.005
Weighted median 0.66 (0.48 to 0.91) 0.011
Weighted mode 0.66 (0.49 to 0.91) 0.124
Simple mode 0.66 (0.48 to 0.91) 0.129
MR Egger 0.65 (0.11 to 3.78) 0.716
CD39 on granulocyte IVW 3 1.37 (1.08 to 1.74) 0.009
Weighted median 1.32 (0.99 to 1.76) 0.059
Weighted mode 1.30 (0.94 to 1.79) 0.258
Simple mode 1.28 (0.90 to 1.82) 0.298
MR Egger 0.72 (0.14 to 3.69) 0.763
CD127 on CD45RA + CD4 +  IVW 4 0.78 (0.61 to 0.99) 0.047
Weighted median 0.74 (0.61 to 0.91) 0.004
Weighted mode 0.67 (0.48 to 0.93) 0.097
Simple mode 0.68 (0.45 to 1.00) 0.147
MR Egger 0.43 (0.04 to 4.48) 0.556
CD8 on CD39 + CD8br IVW 5 1.26 (1.01 to 1.59) 0.044
Weighted median 1.29 (1.02 to 1.64) 0.047
Weighted mode 1.29 (1.04 to 1.61) 0.079
Simple mode 1.25 (1.03 to 1.53) 0.091
MR Egger 0.84 (0.20 to 3.46) 0.825
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All statistical tests were two-sided

MR Mendelian randomization, IVW inverse variance weighted, SNP single nucleotide polymorphism, OR odds ratio, CI confidence intervals.

Bold values indicates the p-value is less than 0.05

On the Monocyte panel, odds ratio (OR) of CX3CR1 on monocyte on HCC risk was estimated to be 0.770 (95% CI 0.6589–0.900, P = 1.031 × 10–3) by using IVW approach. Similar result was observed by using weighted median approach (P = 3.685 × 10–3). OR of CX3CR1 on CD14 + CD16 + monocyte on HCC risk was estimated to be 0.780 (95% CI 0.669–0.910, P = 1.602 × 10–3) by using IVW approach.Similar result was observed by using weighted median approach (P = 1.602 × 10–3). OR of CX3CR1 on CD14 + CD16- monocyte on HCC risk was estimated to be 0.784(95% CI 0.679–0.906, P = 0.981 × 10–3) by using IVW approach. Similar result was observed by using weighted median approach (P = 3.589 × 10–3). We detected a causal efect of CD40 on CD14 + CD16- monocyte on HCC risk by using the IVW approach (OR = 1.155, 95% CI 1.002–1.331, P = 0.047). On the Treg panel, odds ratio (OR) of CD28- CD8br %CD8br on HCC risk was estimated to be 0.663 (95% CI 0.496–0.886, P = 5.434 × 10–3) by using IVW approach. Similar result was observed by using weighted median approach (P = 0.012). OR of CD39 on granulocyte on HCC risk was estimated to be 1.370 (95% CI 1.082–1.736, P = 8.947 × 10–3) by using IVW approach. OR of CD8 on CD39 + CD8br on HCC risk was estimated to be 1.265 (95% CI 1.006–1.590, P = 0.043) by using IVW approach. Similar result was observed by using weighted median approach (P = 0.022). We detected a causal effect of CD127 on CD45RA + CD4 + on HCC risk by using the IVW approach (OR = 0.778, 95% CI 0.607–0.997, P = 0.047). On the TBNK panel, OR of SSC-A on CD4 + on HCC risk was estimated to be 1.393 (95% CI 1.125–1.725, P = 2.387 × 10–3) by using IVW approach. Similar result was observed by using weighted median approach (P = 4.659 × 10–3). OR of FSC-A on CD4 + on HCC risk was estimated to be 1.683 (95% CI 1.175–2.411, P = 4.508 × 10–3) by using IVW approach. Similar result was observed by using weighted median approach (P = 0.012). OR of SSC-A on granulocyte on HCC risk was estimated to be 1.168 (95% CI 1.016–1.342, P = 0.029) by using IVW approach. On the Maturation stages of T cell panel, OR of Naive CD4 + %CD4 + on HCC risk was estimated to be 0.672 (95% CI 0.536–0.842, P = 5.654 × 10–4) by using IVW approach. Similar result was observed by using weighted median approach (P = 1.017 × 10–3). OR of CD8 on EM CD8br on HCC risk was estimated to be 1.243 (95% CI 1.005–1.539, P = 0.045) by using IVW approach. OR of TD DN (CD4-CD8-) %T cell on HCC risk was estimated to be 0.786 (95% CI 0.618–0.999, P = 0.049) by using IVW approach. On the cDC panel, OR of CD11c on granulocyte on HCC risk was estimated to be 1.307 (95% CI 1.046–1.634, P = 0.018) by using IVW approach. Similar result was observed by using weighted median approach (P = 0.022).

Reverse validation and investigation of the causal impact of HCC onset on immunophenotypes

To validate the causal effect of immune cells as an exposure on the outcome—HCC risk—and further explore the impact of hepatocellular carcinoma on immune cells, we employed reverse MR. The selection of instrumental variables was performed using the same parameters as those in the forward MR (Table S1). We identified causal impact of HCC on the levels of 21 immunophenotypes, where HCC onset can increase levels of 9 immune traits and reduce levels of 12 immune traits (Fig. 2). These 21 immune cells were distributed in B cell (6 cells), cDC (2 cells), Maturation stages of T cell 2 cells), Monocyte (2 cells), Myeloid cell (6 cells), TBNK (3cells). Among these immune cells,  hepatocarcinogenesis had the greatest impact on CD4 on EM CD4 + (OR = 0.818,95% CI 0.698–0.958, P = 0.013) and CD33 on Mo MDSC (OR = 1.337,95% CI 1.080–1.656, P = 7.677 × 10 − 3), with CD4 on EM CD4 + decreasing and CD33 on Mo MDSC increasing (Table S2).

Fig. 2.

Fig. 2

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Forest plots showed the causal associations between HCC and immune cell traits

Discussion

Our research incorporated massive amounts of GWAS data, systematically unveiling the mechanisms of immune cells in the initiation and progression of HCC from a genetic point of view. As far as we know, this is among the limited number of studies that have examined the causal association between various immune cells and HCC via MR analysis.

We detected that 15 immune cells had remarkable causal effects on HCC. On the Monocyte panel, CX3CR1 on monocyte, CX3CR1 on CD14 + CD16 + monocyte and CX3CR1 on CD14 + CD16- monocyte could decrease the risk of HCC while CD40 on CD14 + CD16- monocyte could increase the risk of HCC. A research detected that patients with stable angina pectoris displayed significantly elevated CD14 + CD16 + CX3CR1 + monocyte counts compared with patients without vulnerable plaques [23]. Nine months after bare metal stent implantation after acute myocardial infarction, the number of CD14 + CD16 + CX3CR1 + monocytes in patients with restenosis was higher than that in patients without restenosis [24]. Moreover, Pang et al. found that IFNα-producing plasmacytoid dendritic cells drive CX3CR1 + myeloid-derived suppressor cells recruitment and result in neoplasm recurrence after surgical resection in HCC [25]. However, the role of these types of Monocyte cells in HCC has not been validated by biological experiments. On the Treg panel, CD28- CD8br %CD8br and CD127 on CD45RA + CD4 + could decrease the risk of HCC while CD39 on granulocyte and CD8 on CD39 + CD8br could increase the risk of HCC. Mei Sakuma et al. suggested that the change of CD45RA − CD27 + CD127 + central memory T cells frequency during nivolumab treatment might be a biomarker for predicting its therapeutic effect on esophageal squamous cell carcinoma patients [26]. A recent study indicated that high affinity new antigens trigger anti-tumor activity by activating tumor responsive CD39 + CD8 + T cells in hepatocellular carcinoma [27]. However, the role of CD28- CD8br %CD8br and CD39 on granulocyte in HCC has not been verified in HCC yet. On the TBNK panel, SSC-A on CD4 + , FSC-A on CD4 + and SSC-A on granulocyte could increase the risk of HCC. However, the role of SSC-A on CD4 + , FSC-A on CD4 + and SSC-A in the occurrence and development of HCC remains unclear. On the Maturation stages of T cell panel, Naive CD4 + %CD4 + and TD DN (CD4-CD8-) %T cell could decrease the risk of HCC while CD8 on EM CD8br could increase the risk of HCC.CD4 + T cells exert an integral role in immune responses and tissue homeostasis [28]. A recent study suggested that the number of CD4 + T cells displayed a remarkable impact on the survival and prognosis of HCC patients [29]. On the cDC panel, CD11c on granulocyte could increase the risk of HCC. Dendritic cells are the most powerful antigen-presenting cells, which could stimulate naive T cells and drive primary immune response [30]. Due to its capability to stimulate anti-tumor specific T cells, DCs vaccines presented a prospective adjuvant immunotherapy method for HCC patients [31].

The prognosis of HCC was extremely and complexly determined by a wide variety of factors. Precisely, factors like the inherent traits of the tumor itself, the therapeutic approaches, the patient-related elements, and additionally, the post-treatment follow-up and monitoring regimens, all had a crucial and close connection with the final prognosis. One research result indicated that among high-risk populations, compared with not conducting screening, screening for HCC was more effective in reducing the mortality rate of HCC and prolonging the overall survival period [32]. Besides, the portal venous coefficient and the hepatic arterial coefficient of the tumor before hepatectomy were highly valuable for independently forecasting the postoperative survival condition of patients suffering from HCC [33]. Lately, a good number of scholars have developed prognostic models through a series of technological means including gene analysis. Meanwhile, they have also taken part in the research on matters related to immunity, hoping to lend assistance to the prognostic evaluation and treatment of HCC [34, 35]. The findings of our research also implied that certain kinds of immune cells could serve as biomarkers for the risk of HCC, which could have brought about more efficient treatment options and more favorable prognoses.

Moreover, HCC was discovered to have causal effects on 21 immune cells. Notably, it is found that the existence of HCC were closely related to the increased levels of CD33 on Mo MDSC and reduced levels CD4 on EM CD4 + . A recent research indicated that high MDSC infiltration was an independent prognostic risk factor for overall survival and recurrence-free survival in HCC patients [36]. The expression of CD33 on activated macrophages was related to the disease progression and low survival rate of HCC patients [37]. Chaoul et al. found that the total CD4 + T cells and effector memory CD4 + T cells in HCC patients decreased compared with the healthy control groups [38]. Consistent with our findings, the presence of HCC was associated with decreased CD4 on EM CD4 + . However, the specific reasons remain to be studied. The diverse immune cells that exerted an influence on HCC and those that were influenced by HCC did not intersect in any way. This also served as a form of reverse validation, indicating the precise significance of the causal effect.

Limitations

Nevertheless, our research has some weaknesses. First, we chose GWAS aggregated datasets with large immune characteristics and HCC sample sizes. However, the data of immune cells and HCC came from different researches, and there were certain differences in sample size and race, which may lead to some mistakes. The conclusions could not be generalized to other ethnic groups. Second, owing to the lack of individual-level data, we could not implement further hierarchical analysis of the population. Finally, instead of using FDR correction to assess the results, we utilized a loose threshold, which may add some false positives.

Conclusions

The present study furnished valuable proof that immune cells could affect HCC risk via a comprehensive genetic method according to large-scale GWAS aggregated data. Our results expand the understanding of the immunology of HCC onset, providing a new theoretical basis for immunotherapy of HCC.

Supplementary Information

Supplementary material 1 (42KB, xls)
Supplementary material 2 (41.5KB, xls)

Acknowledgements

We would like to express our gratitude to the participants and investigators involved in the GWAS utilized in the present study.

Author contributions

All authors participated in the design, interpretation of the studies and analysis of the data and review of the manuscript; ZW and JZ contributed to the conception and design; MP contributed to the collection of data; ZW and JZ contributed to the data analysis and interpretation. ZW, JZ and CL critically revised the article for essential intellectual content and administrative support. All authors reviewed the manuscript.

Funding

This work was supported by Shandong Provincial Natural Science Foundation (Grant number:ZR2022QH334). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

Data were provided within the manuscript.

Declarations

Ethics approval and consent to participate

Each study incorporated in the GWAS used in the present study was approved by local research ethics committees or Institutional Review Boards, and all participants had given their informed consent.This study does not require additional ethical approval.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jie Zhu, Email: dazhuguo111@163.com.

Changhong Liu, Email: liuchanghong567@126.com.

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Associated Data

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Supplementary Materials

Supplementary material 1 (42KB, xls)
Supplementary material 2 (41.5KB, xls)

Data Availability Statement

Data were provided within the manuscript.

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