Skip to main content
Download PDF

Analysis of the expression and prognosis for leukocyte immunoglobulin-like receptor subfamily B in human liver cancer

World Journal of Surgical Oncology volume 20, Article number: 92 (2022) Cite this article

Abstract

Background

Leukocyte immunoglobulin-like receptor subfamily B (LILRB), including 5 subtypes, is a group of inhibitory receptors in the immune system. The LILRB family is known to be involved in the tumor progression of various cancer types, especially liver cancer. However, the expression patterns and prognostic values of LILRB family members in liver cancer tissues remain unclear.

Methods

We used the Oncomine database, GEPIA database, Kaplan–Meier Plotter, Timer, and TISIDB to assess the expression and prognostic value of the LILRB family in liver cancer patients. We also verified the expression of the LILRB family in tumor tissues and tumor-free liver tissues at the protein level by using immunohistochemistry. The STRING website was used to explore the interaction between the LILRB family and their related genes. The DAVID database was used to perform the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Flow cytometry was used to assess the infiltrated NK cells in liver cancer tissues.

Results

Our study revealed that the mRNA expression of LILRB1, LILRB2, LILRB3, and LILRB5 was downregulated, while compared with normal tissues, the mRNA expression of LILRB4 was upregulated in liver cancer tissues. Survival analysis revealed that LILRB2 and LILRB5 mRNA expression levels were significantly positively associated with overall survival (OS) and disease-free survival (DSS) and that the mRNA expression of all LILRB family members was significantly positively correlated with recurrence-free survival (RFS) and progression-free survival (PFS). Next, we further found that the mRNA expression of all LILRB family members was significantly associated with the infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in liver cancer. Finally, GO and KEGG analyses found that the LILRB family and its related genes were involved in antigen processing and presentation and natural killer cell-mediated cytotoxicity pathways.

Conclusions

Our study suggested that LILRB family expression was associated with the prognosis of liver cancer patients and infiltrated immune cells. The LILRB family might be involved in antigen processing and presentation and natural killer cell-mediated cytotoxicity pathways.

Background

Primary liver cancer is the sixth most diagnosed cancer and the fourth deadliest cancer in the world [1]. Asia and Africa have the highest incidence rates of liver cancer [2, 3]. In China, liver cancer is the fourth most commonly diagnosed cancer [4]. In addition, there are more male patients than female patients. According to pathological types, primary liver cancer is divided into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and HCC-ICC [5]. Populations with hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection, nonalcoholic fatty hepatitis, and alcohol abuse have a high risk of liver cancers. Classic clinical screening methods are alpha-fetoprotein (AFP) and liver ultrasonography. Nuclear medical imaging and liver biopsy are used to estimate the prognosis of liver cancer patients. Considering the heterogeneity of liver cancer, the current method to screen or to predict the prognosis of liver cancer has limitations. Therefore, the identification of alternative and novel biomarkers is urgently needed for successful liver cancer treatment.

The leukocyte immunoglobulin-like receptor B (LILRB) family is a group of immune inhibitory receptors. The LILRB family consists of five members: LILRB1, LILRB2, LILRB3, LILRB4, and LILRB5. In an earlier study, the LILRB family was found to be expressed on many immune cells, such as dendritic cells, macrophages, B cells, T cells, and NK cells [6]. Our previous study also discovered that LILRB2 in CD1c+ myeloid DC subsets was remarkably increased in PBMCs of HCC patients and the microenvironment of liver cancer [7]. Engagement of LILRB1 and LILRB2 with its ligand HLA-G inhibits immune activation, resulting in indirect promotion of tumor development [8]. LILRB1 modulates the differentiation and function of dendritic cells, resulting in poor stimulating activity for primary and memory T cell proliferation [9]. LILRB1 also exhibited immune inhibition on NK cells [10]. Although the ligand for LILRB3 is not so clear, some studies suggested that LILRB3 was associated with cytokeratin-associated proteins exposed to necrotic cancer cells and might be involved in altering the immune responses within the tumor microenvironment [11]. LILRB5 is considered to be an orphan receptor but may be associated with mycobacteria to subvert immune responses [12, 13]. In addition to expression on immune cells, the LILRB family was also found to be expressed in multiple malignant cells, such as liver cancer and lung cancer [14, 15]. Cheng et al. [16] found that LILRB1 in hepatocarcinoma cells might integrate with SH2 domain-containing phosphatase-1 (SHP1) to exert an antitumor effect in liver cancer patients. In contrast, some studies have also shown that LILRB2 regulates tumor cell proliferation, invasion, and migration and promotes tumor progression in lung cancer [17, 18]. LILRB4 was specifically expressed in leukemia cells in monocytic acute myeloid leukemia but not in myelomonocytic acute myeloid leukemia [19].

Therefore, the researches about LILRB family members in cancer tissues are limited and the function of these is not clear. Moreover, the expression levels of the LILRB family and their correlation with clinical features and prognosis have not been reported completely, especially using bioinformatics analysis.

In this study, we aimed to explore the expression of LILRB family members in HCC tissues and find out the relationship between the LILRB family and clinical features and prognosis in HCC patients. Firstly, we used the Oncomine and gene expression profiling interactive analysis (GEPIA) databases to analyze the expression levels of the LILRB family in liver tumor tissues and their relationship with liver tumor stages. Moreover, immunohistochemistry was used to verify the results. Next, Kaplan–Meier Plotter was used to assess whether the LILRB family can be used to predict the prognosis of liver cancer patients. Then, we analyzed the correlation between LILRB family expression and immune cell infiltration by using tumor immune estimation resource (TIMER) and tumor and immune system interaction database (TISIDB). Finally, the retrieval of interacting genes with the LILRB family was exploded by using the search tool for the retrieval of interacting genes (STRING), and the potential signaling pathways related to the LILRB family were predicted through the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

Materials and methods

Oncomine database

The Oncomine platform (www.oncomine.org) is an online cancer microarray database. We used the Oncomine database to analyze the transcriptional levels of the LILRB family in different cancers, especially liver cancer. The conditions of the search filter were as follows: LILRB family gene name, cancer vs. normal analysis, threshold p value, 0.05, threshold fold change, 1.5, threshold gene rank, top 10%, and data-type mRNA.

GEPIA database

GEPIA (gepia.cancer-pku.cn) 2.0 was developed by the Zhang laboratory from Peking University [20]. This online database served to analyze the RNA sequencing expression. All the data were from the TCGA and GTEx projects. In this study, GEPIA 2.0 was used to analyze tumor/normal differential expression and profiling about LILRB family members according to pathological stages.

Kaplan–Meier Plotter

The associations between LILRB family members and survival were plotted as Kaplan–Meier curves using the Kaplan–Meier plotter (kmplot.com/analysis/) [21]. We used this database to analyze the correlation between LILRB family mRNA expression and the overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-free survival (DSS) of liver cancer patients. We also used this database to analyze the relationship between LILRB family mRNA expression and different tumor stages and the prognosis of liver cancer patients. We chose to select the best cutoff to split the LILRB family into a low-expression cohort and a high-expression cohort.

TIMER

TIMER (cistrome.shinyapps.io/timer/) served as a comprehensive resource for the systematic analysis of immune infiltrates across diverse cancer types, including B cells, CD8+ T cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells [22]. TIMER was used to analyze the relationship between LILRB family expression and tumor-infiltrating lymphocytes (TILs) in this study.

TISIDB

TISIDB (cis.hku.hk/TISIDB/) developed by the Zhang laboratory in 2019 was a web portal for tumor and immune cell interactions [23]. All the data were from the PubMed database and The Cancer Genome Atlas (TCGA). We used this web portal to assess the relationship between LILRB family expression and various kinds of TIL subsets in this study.

STRING database

STRING (string-db.org/cgi/input.pl) is an online database of known and predicted protein-protein interactions (PPIs) [24]. STRING was used to construct a PPIs network of the LILRB family. The condition of the minimum required interaction score was set as the highest confidence (0.900). Then, Cytoscape software V3.8.0 was employed to analyze the relationship between LILRB family members and their related genes.

DAVID database

The Database for Annotation, Visualization and Integrated Discovery (DAVID) database (david.ncifcrf.gov/home.jsp) was used to carry out gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis [25]. The R language was used to process the results. A false discovery rate (FDR) < 0.05 was considered to be significant.

Patients and samples

Ten diagnostic liver cancer patients were included in this study. Primary liver cancer tissues and their corresponding tumor-free liver tissues (TFLs) were obtained from surgical resection. The clinical information of these ten diagnostic liver cancer patients was shown in Table.1. This study was approved by the medical ethical committee of the Second Hospital of Nanjing, Nanjing University of Chinese Medicine. Written informed consent was obtained from all donors in accordance with the Declaration of Helsinki (1964) before tissue sample collection.

Table 1 Clinical information of liver cancer patients
Full size table

Immunohistochemistry

Paraffin-embedded liver cancer tissues were sectioned into 3-μm pieces. Tumor sections were incubated with rabbit polyclonal antibodies against LILRB1, LILRB2, LILRB4, and LILRB5 and rat polyclonal antibodies against LILRB3 at 4 °C for 12 h. The antibodies against LILRB1, LILRB3, LILRB4, and LILRB5 were from Abcam (product code: ab229186, ab271287, ab229747, ab121357) and the antibody against LILRB2 was from Invitrogen (product code: PA5-103913). To detect primary antibody binding, the sections were conjugated with goat anti-rabbit IgG H&L (HRP) (Abcam, Cambridge, UK), goat anti-rat IgG H&L (HRP polymer) (Abcam, Cambridge, UK), or goat anti-rabbit IgG (HRP) (Invitrogen, Carlsbad, CA, USA) at 25 °C for 2 h. Finally, the tumor sections were visualized with 3,3-diaminobenzidine and counterstained with hematoxylin. The tumor sections were observed by light microscopy. According to the literature, the H-score was used to evaluate the results [26]. In brief, the calculation method was as follows: percentage of weak staining + 2 × percentage of moderate staining + 3 × percentage of strong staining, giving a range of 0–300.

Isolation of single cells and flow cytometry analysis

Liver tumor tissues and TFL were cut into approximately 1-mm3 pieces and digested with 0.125 mg/ml collagenase IV (Sigma-Aldrich, St. Louis, MO) and 0.2 mg/ml DNAse I (Roche, Indianapolis, IN) in Hanks solution for 1 h. Then, cells were isolated by filtration of liver slurry through 40-μm filters (BD Biosciences, San Jose, CA, USA), followed by centrifugation at 400 g for 10 min. These cells were washed twice with PBS (pH = 7.4) with 1% FBS (Invitrogen, Carlsbad, CA, USA). Next, cells were labeled with monoclonal antibodies, including CD3-PerCp (OKT3), CD56-PeCy7 (MEM-188), CD16-APC-Cy7 (CB16), and LIVE/DEAD® Aqua, for 30 min at 4 °C (Thermo Fisher Scientific, Waltham, MA, USA). After washing twice in PBS with 1% FBS, cells were analyzed by a FACS Canto II flow cytometer (BD Biosciences, San Jose, CA, USA).

Statistical analysis

The percentage of NK cell subsets are expressed as the mean ± SEM. Student’s 𝑡-test was used to analyze the results of immunohistochemistry and the difference of NK cell subsets between tumor and their corresponding TFL. Significance was defined as < 0.05.

Results

Transcriptional levels of the LILRB family in patients with liver cancer

The LILRB family contains five subtypes: LILRB1, LILRB2, LILRB3, LILRB4, and LILRB5. We compared the mRNA expression of the LILRB family in different cancers with that in corresponding normal tissues based on the Oncomine database. As shown in Fig. 1, compared with that of normal tissues, in addition to LILRB4, other LILRB family members were downregulated in liver tumor tissues. All the data came from four datasets, and all the tumor data came from HCC [27,28,29,30]. Three of the datasets had the same results even if two datasets did not achieve the condition of fold change > 1.5, while Mas’s dataset had the opposite results (Table 2).

Fig. 1
figure 1

The transcription levels of the LILRB family in different types of human cancers. The figure was generated from the Oncomine database under the conditions of p < 0.05, fold change > 1.5, gene bank: top 10%, data type: mRNA. The cell number represented the dataset numbers that meet the conditions. The cell color was determined by the best gene rank percentile for the analyses within the cell. Blue represents underexpression and red represents overexpression

Full size image
Table 2 Datasets of the LILRB family mRNA expression in Liver cancer (Oncomine database)
Full size table

To verify the above results further, GEPIA 2.0 was used to compare the mRNA expression of the LILRB family between liver tumor tissues and normal tissues. The LIMMA method was used to compare liver tumor tissues and their paired normal samples regarding mRNA expression of the LILRB family. The results showed that LILRB1, LILRB2, LILRB3, and LILRB5 were downregulated, and compared with liver normal tissues, LILRB4 expression was upregulated in liver tumor tissues, which was consistent with the results in the Oncomine database (Fig. 2).

Fig. 2
figure 2

The transcription expression levels of the LILRB family in liver cancer (GEPIA 2.0). A Scatter diagram of individual LILRB member expression. T means tumor tissue and N means normal tissue. The number of HCC tumor tissues and normal tissues was noted in the figure. B Box plot of individual LILRB member expression. The red color represented tumor tissues, and the gray color represented normal tissues. The number of HCC tumor samples was 369 and the number of normal tissues was 160. This figure was generated under the conditions of log2(fold change) cutoff: 1, q-value cutoff: 0.01. The ANOVA was used for tumor vs paired normal samples. *p < 0.05

Full size image

To assess LILRB expression at the protein level, we detected LILRB family expression in liver tumors and their corresponding TFLs by using immunohistochemistry. We found that the expression of LILRB1, LILRB2, LILRB3 and LILRB5 was lower in liver tumors than in TFL, while the expression of LILRB4 was high in liver tumors than in TFL (Fig. 3).

Fig. 3
figure 3

The expression of LILRB family members in liver cancer by immunohistochemical analysis. A Representative IHC images of LILRB family members’ expression in the microarray that contained 10 cases of liver tumor tissues and their TFL. Multiple images were taken and a representative one was presented. Brown granules were defined as positive staining of LILRB family members. The magnification was 100×. B The histogram of H-score about each LILRB family members. Student’s t test was used for tumor vs TFL samples. *p < 0.05, **p<0.01, ***p<0.001

Full size image

The relationship between the LILRB family and tumor stage in liver cancer patients

We compared the relationship between LILRB family expression and four tumor stages in liver cancer patients by using GEPIA 2.0. One-way ANOVA was used to analyze these results. In agreement with the literature [16], although there was no significant correlation between LILRB family expression and tumor stage in liver cancer patients, compared with that in other stages of liver cancer patients, the expression of the LILRB family in stage IV liver cancer patients decreased (Fig. 4).

Fig. 4
figure 4

The correlation between LILRB family expression and tumor stages in liver cancer patients (GEPIA 2.0). Patients with liver cancer were divided into four groups based on disease stage, including stages I, II, III, and IV. The correlation between LILRB family members’ expression and tumor stages was analyzed by one-way ANOVA.

Full size image

Prognostic value of LILRB family mRNA expression in all liver cancers

Kaplan–Meier Plotter was used to examine the prognostic value of LILRB family mRNA expression levels in all liver cancers. We compared the correlation between mRNA expression of LILRB family members and OS, RFS, PFS, and DSS of liver cancer patients. The results revealed that patients in LILRB2- and LILRB5-low groups had shortened OS and DSS and low expression of all LILRB family members predicted poorer patients’ RFS and PFS (p < 0.05) (Fig. 5). Median survival times of LILRB-high groups in OS, PFS, PFS, and DSS were longer than those of LILRB-low groups, when there was a statistical difference between these two groups (Table 3).

Fig. 5
figure 5

Comparison of Kaplan–Meier survival curves of LILRB family members’ low expression and high expression in liver cancers (Kaplan–Meier Plotter). The expression of LILRB family members related to OS(n = 364), RFS(n = 316), PFS(n = 370) and DSS(n=362) in liver cancers. Transcriptional expression levels of the LILRB family and patient survival information were obtained from GEO, EGA, and TCGA. The best cutoff values were auto-selected by the tool of Kaplan–Meier Plotter

Full size image
Table 3 Median survival time of LILRB low- and high-expression liver patients in OS, RFS, PFS, and DSS (Kaplan–Meier plots)
Full size table

Next, we further investigated the prognostic value of LILRB family mRNA expression in liver cancer with different histologic stages by using Kaplan–Meier Plotter. The results were as follows: (1) The high mRNA expression of LILRB1 was associated with better RFS and PFS in stages I and II. (2) The high mRNA expression of LILRB2 was associated with longer OS, RFS and PFS in stages I, II, and III. High mRNA expression of LILRB2 was predicted to have better DSS in stages II and III. (3) High expression of LILRB3 mRNA was correlated with better RFS and PFS in stage II, while low expression of LILRB3 mRNA was associated with better DSS in stage I. (4) The low mRNA expression of LILRB4 was associated with better DSS in stage I. However, the high expression of LILRB4 mRNA was correlated with better RFS and PFS in stage II as well as better PFS in stage III. (5) High mRNA expression of LILRB5 was predicted to be associated with better OS in stage II and III patients and with better PFS in stage II patients, while low mRNA expression of LILRB5 was associated with better OS in stage I patients (Supplementary Table S1).

The relationship between LILRB family expression and immune cell infiltration

The LILRB family is considered to be an immune inhibitory receptor. TILs are associated with prognostic indicators for liver cancer [31]. Therefore, we speculated that there was a positive correlation between LILRB family expression and TIL infiltration in liver cancer patients. TIMER database was used to analyze the suspected association. The results showed that all LILRB family members were significantly associated with tumor purity, B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (Fig. 6).

Fig. 6
figure 6

Correlation of LILRB family expression with immune infiltration level in liver cancer (TIMER). LILRB family members’ expression in HCC tissues negatively correlated with tumor purity and positively correlated with infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (n = 371). The correlations were analyzed using Spearman’s correlation measure. LIHC, liver hepatocellular carcinoma

Full size image

The TISIDB database was used to assess the relationship between LILRB family expression and TIL subsets. Although there was no significant correlation of LILRB5 expression with activated CD4+ T cells and CD56dim natural killer cells, the expression of other LILRB family members in liver cancer was strongly associated with various TIL subsets (Supplementary Tables S2, S3, S4, S5, S6). Together these findings suggested that the LILRB family plays an important role in the recruitment and regulation of immune infiltrating cells in liver cancer.

Predicted functions and pathways of the LILRB family in liver cancer

To understand the biological significance and consequences of the protein profiling of the LILRB family, we constructed a PPI network by using STRING. The Cytoscape software was used to process it. As shown in Fig. 7, PPI analysis showed known and predicted interactions between LILRB family members and 50 proteins.

Fig. 7
figure 7

Analysis of LILRB family-related proteins based on a protein-protein interaction network. Using the STRING database, a total of 50 proteins were filtered into the protein PPI network. Nodes represent interacting proteins, and the 452 edges represent known or predicted interactions

Full size image

Next, GO and KEGG analyses for the LILRB family and these 50 genes were performed using the DAVID online tool. The GO analysis showed that there were 38 items of biological process (BP), 25 items of cellular component (CC), and 23 items of molecular function (MF) with FDR less than 0.05. The first 10 items were used to plot. The GO analysis for BP showed that LILRB family members and their related genes were mostly enriched in the immune response and antigen processing and presentation of antigens (Fig. 8A). The GO analysis for MF showed that LILRB family members and their related genes were associated with beta-2-microglobulin binding, peptide antigen binding, and MHC class I protein binding (Fig. 8B). The GO analysis for CC revealed that LILRB family members and their related genes were significantly enriched in the plasma membrane and cell surface (Fig. 8C). In KEGG analysis, 27 pathways were related to the function of LILRB family members (Fig. 8D). Among them, antigen processing and presentation, cell adhesion molecules (CAMs), phagosomes, natural killer cell-mediated cytotoxicity, and leukocyte transendothelial migration were involved in antitumor immunity in liver cancer.

Fig. 8
figure 8

GO and KEGG analyses of the LILRB family and their related genes. The DAVID online database was used to perform the GO and KEGG analyses. A–C The top 10 items of the GO analysis for BP, CC, and MF of the LILRB family and their related genes, respectively. D The KEGG pathways of the LILRB family and their related genes

Full size image

Based on the KEGG results and previous researches [32,33,34], we hypothesized that LILRB family members and their related genes are involved in the pathways of antigen processing and presentation and natural killer (NK) cell-mediated cytotoxicity.

Our previous study verified that compared with their corresponding tumor-free liver tissues (TFLs), the percentage of infiltrating CD1c+ myeloid DCs (mDCs) was significantly decreased in liver tumor tissues [7]. Considering that infiltrating NK cells in liver cancer might be involved in the signaling of the LILRB family, we further compared the percentage of NK cells in liver tumor tissues and the corresponding TFL. Human NK cells can be segregated into three major subsets: CD56brightCD16dim/−, CD56dimCD16+, CD56dimCD16dim/− NK cells. The strategy of gating these three NK cells subsets were according to the literature [35]. The results shown that the percentage of CD56brightCDdim/− NK cell in liver tumor tissues was lower than that from TFL tissues (Tumor vs TFL: 30.84 % ± 7.77 % vs 43.85 % ± 4.12 %, p < 0.05) (Fig. 9). In contrast, the percentage of CD56dimCD16+ NK cell in liver tumor tissues was higher than that from TFL tissues (Tumor vs TFL: 49.98 % ± 8.66 % vs 29.73 % ± 4.78 %, p < 0.05).

Fig. 9
figure 9

The expression of NK cell subsets in liver cancer. A Flow cytometry was used to detect NK cells. NK cell subsets were identified by three unique CD56 and CD16 expression patterns in the gate of Live+CD3-CD56bright/dim. B The percentage of NK cell subsets from liver tumors and the corresponding TFL (n = 10). *p < 0.05

Full size image

Discussion

The LILRB family is considered to be an immune inhibitory receptor. Most studies have focused on the expression of the LILRB family on immune cells in many cancers [19, 36]. A recent study showed that LILRB2 was expressed in lung cancer tissues and that its expression resulted in poor patient OS [37]. On the contrary, Cheng et al. [16] found that LILRB1 in hepatocarcinoma cells might integrate with SHP1 to exert an antitumor effect in liver cancer patients. Hence, the expression of LILRB family members have observed in tumor cells with no clear function. Moreover, the expression of all LILRB family members and their relationships with prognosis in liver cancer patients have yet to be determined. In this study, a series of bioinformatics analysis methods and experiments were used to explode the expression, prognosis and potential function of the LILRB family in liver cancer.

First, we assessed the expression of the LILRB family in liver tumor tissues and its correlation with the survival of liver cancer patients. The Oncomine and GEIPA 2.0 databases confirmed that LILRB1, LILRB2, LILRB3, and LILRB5 expression was lower in liver tumor tissues than in normal tissues and that LILRB4 was overexpressed in liver tumor tissues. To further verify these results, we assessed LILRB family member expression at the protein level by using immunohistochemistry. The results were in accordance with the mRNA expression of the LILRB family, which was also consistent with a previous study [16]. In contrast, some studies proved that LILRB2 was expressed at higher levels in HCC tissues, which needs further verification with large samples in future studies [37, 38]. In addition, we also used Kaplan–Meier Plotter to explode the prognostic merit of LILRB family member expression in liver cancer. Our results showed that LILRB2 and LILRB5 expression was positively associated with OS and DSS and that the mRNA expression of all LILRB family members was significantly positively correlated with RFS and PFS in liver cancer patients. Hence, these results robustly indicated that the LILRB family was a potential prognostic biomarker in liver cancer patients.

Numerous studies have proven that the LILRB family plays an immunosuppressive role in the immune system. These immune inhibitory receptors could restrict T cell infiltration and killing ability, leading to impaired antitumor responses [39]. Scientists have discovered that PIR-B is expressed on murine B cells and myeloid cells, as the ortholog for human LILRB1/B2, blocked the access of CD8αα to MHC-I, resulting in weaker antitumor immunity [40]. Researchers have also found that disruption of either MHC class I or LILRB1 enhanced phagocytosis of tumor cells in macrophages [36]. For these reasons, we compared the correlation between the LILRB family and infiltrating immune cells. The mRNA expression of the LILRB family was inversely proportional to the purity of the liver tumor, while LILRB family expression was proportional to immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. The positive correlation between LILRB family members’ expression and immune cells implicated the role of LILRBs in recruiting and regulating tumor immunology in liver cancer.

Previous studies have explored some mechanisms of the LILRB family expressed on immune cells in the immune system. Khanolkar et al. found that LILRB1 on dendritic cells increased the expression of the NF-κB regulator ABIN1/TNIP1, resulting in suppression of the stimulatory effect of dendritic cells on T cells. Research has also discovered that LILRB2 expressed on tumor-associated myeloid cells inhibited the activation of SHP1/2, AKT and STAT6, leading to restraint of the function of M1-like macrophages and promotion of the function of M2-like macrophages [41]. However, these mechanisms are concerned with the expression of the LILRB family on immune cells, and the mechanisms of the LILRB family expressed in the tumor microenvironment, especially on solid tumors, have not yet been discovered clearly. We identified 50 potential and validated proteins that interacted with the LILRB family by using STRING. The KEGG analysis results and previous experiments showed that the LILRB family might be involved in antigen processing and presentation and NK cell pathways [42]. NK cells have been studied as three separate populations: CD56brightCD16dim/−, CD56dimCD16+, CD56dimCD16dim/− NK cells. CD56brightCD16dim/− NK cells secreted cytokines to kill tumor cells and regulate the immune system. CD56dimCD16+ NK cells primarily demonstrated cytotoxicity effect. CD56dimCD16dim/− NK cells might be the immediate precursors of the CD56dimCD16+ subset, which remained to be clarified in the future [43]. Consistent with the literature, CD56dimCD16+ NK cells were significantly reduced in liver tumor tissues compared with TFL, resulting in secreting less cytokines that killed tumor cells [44]. This might due to the reduced expressed LILRB family members on tumor cells, leading to recruiting less NK cells.

Conclusions

In this study, using bioinformatics analysis, we identified decreases in LILRB1, LILRB2, LILRB3, and LILRB5 and increases in LILRB4 in liver tumor tissues, which were confirmed at the mRNA and protein levels. Moreover, survival analysis revealed that LILRB2 and LILRB5 mRNA levels were significantly positively associated with OS and DSS and that the mRNA expression of all LILRB family members was significantly positively correlated with RFS and PFS. High expression of LILRB family members was associated with increased infiltration of immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Finally, PPI prediction analysis of the LILRB family and KEGG analysis suggested that LILRB family members might be involved in antigen processing and presentation and natural killer cell pathways during the development of liver cancer.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

All the databases used in this study were below:

Oncomine database: www.oncomine.org

GEPIA database: http://gepia2.cancer-pku.cn/#index

Kaplan–Meier Plotter: http://kmplot.com/analysis/index.php?p=service&cancer=liver_rnaseq

Timer: https://cistrome.shinyapps.io/timer/

TISIDB: http://cis.hku.hk/TISIDB/

STRING database: https://string-db.org/cgi/input.pl

DAVID database: https://david.ncifcrf.gov/summary.jsp

Abbreviations

LILRB:

Leukocyte immunoglobulin-like receptor subfamily B

HCC:

Hepatocellular carcinoma

ICC:

Intrahepatic cholangiocarcinoma

HBV:

Hepatitis B virus

HCV:

Hepatitis C virus

AFP:

Alpha-fetoprotein

GEPIA:

Gene expression profiling interactive analysis

OS:

Overall survival

RFS:

Relapse-free survival

PFS:

Progression-free survival

DSS:

Disease-free survival

Timer:

Tumor immune estimation resource

TILs:

Tumor-infiltrating lymphocytes

PPI:

Protein-protein interactions

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

CAMs:

Cell adhesion molecules

APCs:

Antigen-presenting cells

NK cells:

Natural killer cells

TFL:

Tumor-free liver tissues

References

  1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: A Cancer J Clin. 2018;68(6):394–424.

    Google Scholar 

  2. Mohammadian M, Soroush A, Mohammadian-Hafshejani A, et al. Incidence and mortality of liver cancer and their relationship with development in Asia [J]. Asian Pacific J Cancer Prev. 2016;17(4):2041–7.

    Article  Google Scholar 

  3. Mak D, Sengayi M, Chen WC, et al. Liver cancer mortality trends in South Africa: 1999–2015[J]. BMC Cancer. 2018;18(1):798.

    Article  Google Scholar 

  4. Feng R-M, Zong Y-N, Cao S-M, et al. Current cancer situation in China: good or bad news from the 2018 Global Cancer Statistics?[J]. Cancer Commun. 2019;39(1):22.

    Article  Google Scholar 

  5. Zhou J, Sun H-C, Wang Z, et al. Guidelines for diagnosis and treatment of primary liver cancer in China (2017 Edition)[J]. Liver Cancer. 2018;7(3):235–60.

    Article  Google Scholar 

  6. Jones DC, Kosmoliaptsis V, Apps R, et al. HLA class I allelic sequence and conformation regulate leukocyte Ig-like receptor binding [J]. J Immunol. 2011;186(5):2990.

    Article  CAS  Google Scholar 

  7. Wang L, Fan J, Ye W, Han J, Zhang Y, Zhao L, Duan J, Yin D, Yi Y.  The expression of ILT4 in myeloid dendritic cells in patients with hepatocellular carcinoma. Immunol Invest. 2019;48(7):704–18.

  8. Burshtyn DN, Morcos C. The expanding spectrum of ligands for leukocyte Ig-like receptors [J]. J Immunol. 2016;196(3):947.

    Article  CAS  Google Scholar 

  9. Young NT, Waller ECP, Patel R, et al. The inhibitory receptor LILRB1 modulates the differentiation and regulatory potential of human dendritic cells [J]. Blood. 2008;111(6):3090–6.

    Article  CAS  Google Scholar 

  10. Chen H, Chen Y, Deng M, et al. Antagonistic anti-LILRB1 monoclonal antibody regulates antitumor functions of natural killer cells [J]. J Immunother Cancer. 2020;8(2):e000515.

    Article  Google Scholar 

  11. Jones DC, Hewitt CRA, López-Álvarez MR, Jahnke M, Russell AI, Radjabova V, Trowsdale ARZ, Trowsdale J. Allele-specific recognition by LILRB3 and LILRA6 of a cytokeratin 8-associated ligand on necrotic glandular epithelial cells. Oncotarget. 2016;7(13):15618.

  12. Hogan LE, Jones DC, Allen RL. Expression of the innate immune receptor LILRB5 on monocytes is associated with mycobacteria exposure [J]. Sci Rep. 2016;6(1):21780.

    Article  CAS  Google Scholar 

  13. Yeboah M, Papagregoriou C, Jones DC, et al. LILRB3 (ILT5) is a myeloid cell checkpoint that elicits profound immunomodulation [J]. JCI Insight. 2020;5(18):e141593.

    Article  Google Scholar 

  14. Kang X, Kim J, Deng M, et al. Inhibitory leukocyte immunoglobulin-like receptors: immune checkpoint proteins and tumor sustaining factors [J]. Cell Cycle. 2016;15(1):25–40.

    Article  CAS  Google Scholar 

  15. Wiśniewski A, Kowal A, Wyrodek E, et al. Genetic polymorphisms and expression of HLA-G and its receptors, KIR2DL4 and LILRB1, in non-small cell lung cancer [J]. Tissue Antigens. 2015;85(6):466–75.

    Article  Google Scholar 

  16. Cheng J, Luan J, Chen P, et al. Immunosuppressive receptor LILRB1 acts as a potential regulator in hepatocellular carcinoma by integrating with SHP1[J]. Cancer Biomarkers. 2020;28:309–19.

    Article  CAS  Google Scholar 

  17. Liu X, Yu X, Xie J, et al. ANGPTL2/LILRB2 signaling promotes the propagation of lung cancer cells [J]. Oncotarget. 2015;6(25):21004–15.

    Article  Google Scholar 

  18. Zhang P, Guo X, Li J, et al. Immunoglobulin-like transcript 4 promotes tumor progression and metastasis and up-regulates VEGF-C expression via ERK signaling pathway in non-small cell lung cancer [J]. Oncotarget. 2015;6(15):13550–63.

    Article  Google Scholar 

  19. John S, Chen H, Deng M, et al. A novel anti-LILRB4 CAR-T cell for the treatment of monocytic AML [J]. Mole Therapy. 2018;26(10):2487–95.

    Article  CAS  Google Scholar 

  20. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses [J]. Nucleic Acids Res. 2017;45(W1):W98–W102.

    Article  CAS  Google Scholar 

  21. Nagy LA, Menyhárt O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets [J]. Sci Rep. 2018;8(1):9227.

    Article  Google Scholar 

  22. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells [J]. Cancer Res. 2017;77(21):e108–10.

    Article  CAS  Google Scholar 

  23. Ru B, Wong CN, Tong Y, et al. TISIDB: an integrated repository portal for tumor–immune system interactions [J]. Bioinformatics. 2019;35(20):4200–2.

    Article  CAS  Google Scholar 

  24. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets [J]. Nucleic Acids Res. 2018;47(D1):D607–13.

    Article  Google Scholar 

  25. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources [J]. Nat Protoc. 2009;4(1):44–57.

    Article  CAS  Google Scholar 

  26. Pirker R, Pereira JR, Von Pawel J, et al. EGFR expression as a predictor of survival for first-line chemotherapy plus cetuximab in patients with advanced non-small-cell lung cancer: analysis of data from the phase 3 FLEX study [J]. Lancet Oncol. 2012;13(1):33–42.

    Article  CAS  Google Scholar 

  27. Wurmbach E, Chen Y-B, Khitrov G, et al. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma [J]. Hepatology. 2007;45(4):938–47.

    Article  CAS  Google Scholar 

  28. Roessler S, Jia H-L, Budhu A, et al. A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients [J]. Cancer Res. 2010;70(24):10202.

    Article  CAS  Google Scholar 

  29. Mas VR, Maluf DG, Archer KJ, et al. Genes involved in viral carcinogenesis and tumor initiation in hepatitis C virus-induced hepatocellular carcinoma [J]. Mole Med (Cambridge, Mass.). 2009;15(3-4):85–94.

    CAS  Google Scholar 

  30. Chen X, Cheung ST, So S, et al. Gene expression patterns in human liver cancers [J]. Mole Biol Cell. 2002;13(6):1929–39.

    Article  CAS  Google Scholar 

  31. Yao W, He J-C, Yang Y, et al. The prognostic value of tumor-infiltrating lymphocytes in hepatocellular carcinoma: a systematic review and meta-analysis [J]. Sci Rep. 2017;7(1):7525.

    Article  Google Scholar 

  32. Gu X, Laouar A, Wan J, et al. The gp49B1 inhibitory receptor regulates the IFN-γ responses of T cells and NK cells [J]. J Immunol. 2003;170(8):4095.

    Article  CAS  Google Scholar 

  33. Si YQ, Bian XK, Lu N, et al. Cyclosporine induces up-regulation of immunoglobulin-like transcripts 3 and 4 expression on and activity of NKL cells [J]. Transplant Proc. 2012;44(5):1407–11.

    Article  CAS  Google Scholar 

  34. Jin Z, Fan J, Zhang Y, et al. Comparison of morphology, phenotypes and function between cultured human IL4DC and IFNDC [J]. Mol Med Rep. 2017;16(5):7345–54.

    Article  CAS  Google Scholar 

  35. Vujanovic L, Chuckran C, Lin Y, Ding F, Sander CA, Santos PM, Lohr J, Mashadi-Hossein A, Warren S, White A. CD56dim CD16− natural killer cell profiling in melanoma patients receiving a cancer vaccine and interferon-α. Front Immunol. 2019;10:14.

  36. Barkal AA, Weiskopf K, Kao KS, et al. Engagement of MHC class I by the inhibitory receptor LILRB1 suppresses macrophages and is a target of cancer immunotherapy [J]. Nat Immunol. 2018;19(1):76–84.

    Article  CAS  Google Scholar 

  37. Li Q, Li J, Wang S, et al. Overexpressed immunoglobulin-like transcript (ILT) 4 in lung adenocarcinoma is correlated with immunosuppressive T cell subset infiltration and poor patient outcomes [J]. Biomarker Res. 2020;8:11.

    Article  CAS  Google Scholar 

  38. Li X, Wei X, Xu H, et al. Expression of leukocyte immunoglobulin-like receptor B2 in hepatocellular carcinoma and its clinical significance [J]. J Cancer Res Therapeut. 2018;14(7):1655–9.

    Article  CAS  Google Scholar 

  39. Thommen DS, Schumacher TN. T cell dysfunction in cancer [J]. Cancer cell. 2018;33(4):547–62.

    Article  CAS  Google Scholar 

  40. Endo S, Sakamoto Y, Kobayashi E, et al. Regulation of cytotoxic T lymphocyte triggering by PIR-B on dendritic cells [J]. Proc National Acad Sci. 2008;105(38):14515–20.

    Article  CAS  Google Scholar 

  41. Chen H-M, Van Der Touw W, Wang YS, et al. Blocking immunoinhibitory receptor LILRB2 reprograms tumor-associated myeloid cells and promotes antitumor immunity [J]. J Clin Invest. 2018;128(12):5647–62.

    Article  Google Scholar 

  42. Hoechst B, Voigtlaender T, Ormandy L, et al. Myeloid derived suppressor cells inhibit natural killer cells in patients with hepatocellular carcinoma via the NKp30 receptor [J]. Hepatology. 2009;50(3):799–807.

    Article  CAS  Google Scholar 

  43. Amand M, Iserentant G, Poli A, Sleiman M, Fievez V, Sanchez IP, Sauvageot N, Michel T, Aouali N, Janji B. Human CD56dimCD16dim cells as an individualized natural killer cell subset. Front Immunol. 2017;8:699.

  44. Cai L, Zhang Z, Zhou L, et al. Functional impairment in circulating and intrahepatic NK cells and relative mechanism in hepatocellular carcinoma patients [J]. Clin Immunol. 2008;129(3):428–37.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by Science and Technology Commission of Nanjing grant number 201803001] and Medical Science and Technology Development Foundation, Nanjing Department of Health grant number YKK17167 and YKK21119].

Author information

Author notes

  1. Jing Fan and Lili Wang contributed equally to this work.

  2. Jing Fan and Lili Wang are co-first authors.

Authors and Affiliations

  1. Clinical Research Center, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Zhong Fu Road, Gulou District, Nanjing, Jiangsu, People’s Republic of China, 210003

    Jing Fan, Lili Wang, Jiayan Li & Dandan Yin

  2. Nanjing University of Chinese Medicine, Han Zhong Road, Jianye District, Nanjing, Jiangsu, People’s Republic of China, 210029

    Miao Chen & Jiakang Zhang

  3. Department of Hepatobiliary Surgery, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Zhong Fu Road, Gulou District, Nanjing, Jiangsu, People’s Republic of China, 210003

    Fangnan Song, Aidong Gu & Yongxiang Yi

Authors
  1. Jing Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lili Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiakang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiayan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fangnan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aidong Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dandan Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yongxiang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.L.W. designed the experiments and wrote the paper. M.C., J.K.Z., and J.Y.L. collected the data. F.N.S. and A.D.G. collected the clinical samples. D.D.Y. analyzed the data. J.F. and Y.X.Y. conducted the experiments and edited the paper. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Yongxiang Yi.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the medical ethical committee of the Second Hospital of Nanjing, Nanjing University of Chinese Medicine. Written informed consent was obtained from all donors in accordance with the Declaration of Helsinki (1964) before tissue sample collection.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fan, J., Wang, L., Chen, M. et al. Analysis of the expression and prognosis for leukocyte immunoglobulin-like receptor subfamily B in human liver cancer. World J Surg Onc 20, 92 (2022). https://doi.org/10.1186/s12957-022-02562-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12957-022-02562-w

Keywords

  • Liver cancer
  • LILRB
  • Prognosis
  • Bioinformatics analysis
  • NK cells

World Journal of Surgical Oncology

ISSN: 1477-7819

Contact us