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. 2024 Feb 6;18(1):23–39. doi: 10.1049/syb2.12086

The ‘Other’ subfamily of HECT E3 ubiquitin ligases evaluate the tumour immune microenvironment and prognosis in patients with hepatocellular carcinoma

Runyu Dong 1, Zhixiong Wang 1, Danping Cao 1, Yanna Li 1, Yao Fei 1, Peng Gao 1, Menglin Zhu 1, Zhiqiang Chen 2,✉, Juan Cai 3,4,✉, Xueliang Zuo 1,3,✉
PMCID: PMC10860721  PMID: 38318939

Abstract

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer‐related death worldwide. The role of the ‘Other’ subfamily of HECT E3 ligases (E3s) in hepatocellular carcinoma (HCC) remains unknown. The expression of the ‘Other’ HECT E3s was performed using The Cancer Genome Atlas (TCGA) data, and the authors found that the ‘Other’ HECT E3s were differentially expressed in HCC. Prognostic values were assessed using the Kaplan–Meier method and indicated that the high expressions of HECTD2, HECTD3, and HACE1 were associated with a worse clinical prognosis of HCC patients. The expression of HECTD2 was significantly correlated with the infiltration of CD4+T cells and neutrophils. The levels of HECTD3 and HACE1 were notably related to the dendritic cells and memory B cells infiltrated in HCC. In addition, the three previously mentioned genes have shown to be associated with immune checkpoint genes, such as FOXP3, CCR8, STAT5B, TGFB1 and TIM‐3. Moreover, HECTD2 could promote the proliferative activity, cell migration and invasive ability of HCC cells. Collectively, the authors’ study demonstrated that HECTD2 was a novel immune‐related prognostic biomarker for HCC, providing new insight into the treatment and prognosis of HCC.

Keywords: bioinformatics, cancer, liver

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer‐related death worldwide. The role of the ‘Other’ subfamily of HECT E3 ligases (E3s) in hepatocellular carcinoma (HCC) remains unknown. The expression of the ‘Other’ HECT E3s was performed using The Cancer Genome Atlas (TCGA) data, and the authors found that the ‘Other’ HECT E3s were differentially expressed in HCC. Prognostic values were assessed using the Kaplan–Meier method and indicated that the high expressions of HECTD2, HECTD3, and HACE1 were associated with a worse clinical prognosis of HCC patients. The expression of HECTD2 was significantly correlated with the infiltration of CD4+T cells and neutrophils. The levels of HECTD3 and HACE1 were notably related to the dendritic cells and memory B cells infiltrated in HCC. In addition, the three previously mentioned genes have shown to be associated with immune checkpoint genes, such as FOXP3, CCR8, STAT5B, TGFB1 and TIM‐3. Moreover, HECTD2 could promote the proliferative activity, cell migration and invasive ability of HCC cells. Collectively, the authors’ study demonstrated that HECTD2 was a novel immune‐related prognostic biomarker for HCC, providing new insight into the treatment and prognosis of HCC.

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Abbreviations

BP

Biological process

CC

Cell composition

CCK‐8

Cell counting kit‐8

E3s

E3 ubiquitin ligases

EdU

5‐ethynyl‐2′‐deoxyuridine

GO

Gene Ontology

HCC

Hepatocellular carcinoma

HECT

Homologous to the E6AP carboxyl terminus

ICB

Immune checkpoint blockade.

KEGG

Kyoto encyclopaedia of genes and genomes

KM

Kaplan–Meier

MDSC

Myeloid‐derived immunosuppressive cells

MF

Molecular function

OS

Overall survival

PFS

Progression‐free survival

qRT‐PCR

Quantitative real‐time PCR

TAM

Tumour‐associated macrophage

Th1

Type 1T helper

Th2

Type 2T helper

TILs

Tumour‐infiltrating lymphocytes

TMB

Tumour mutational burden

Tregs

Regulatory T cells.

1. INTRODUCTION

Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer‐related death worldwide, with approximately 906,000 new cases and 830,000 deaths [1]. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and accounts for 90% of the cases [2]. Immunotherapy for HCC has grown dramatically over the last decade, offering great promises to the treatment of liver cancer patients [3]. However, the early diagnosis of HCC remains low, and the 5‐year survival rate is only 18% [4, 5]. Therefore, it is essential to determine more accurate biomarkers for HCC immunotherapy.

E3 ubiquitin ligases (E3s) are the key components of the ubiquitination cascade, which strictly controls the efficiency and substrate specificity of the ubiquitination reaction. They are generally divided into three classes: the really interesting new gene (RING)—type E3s, the homologous to the E6AP carboxyl terminus (HECT)—type E3s, and the RING‐between‐RING (RBR)—type E3s [6]. In addition, the human HECT E3s have been grouped into three subfamilies based on the structure of their N‐terminal substrate‐binding domains: ‘NEDD4’, ‘HERC’, and the ‘Other’ subfamilies. The ‘NEDD4’ and ‘HERC’ subfamilies have the same N‐terminal domains, but they are respectively characterised by the WW and C2 domains and the RCC1‐like domain (RLD). The ‘Other’ subfamily contains 13 members, lacking the WW or RLD domains, and have diverse N‐terminal regions [7]. Here, we focus on the function of eight less‐studied genes of the ‘Other’ subfamily in cancer, which are E6AP, UBE3B, UBE3C, HECTD1, HECTD2, HECTD3, HECTD4, and HACE1 [8].

E6AP, one of the founding members of the HECT E3s family, is a 100 kDa cellular protein encoded by the the UBE3A gene that exhibits aberrant activity in multiple cancers. It drives tumourigenesis by targeting multiple crucial tumour factors in prostate cancer, non‐small cell lung cancer and lymphoma as an oncogene [9]. It has been shown that the expression level of UBE3B differed 7‐ to 8‐fold in cancer cell lines compared to normal cell lines, particularly in the LN428 glioblastoma cell line. Furthermore, UBE3B knockdown sensitises glioma cells to clinically achievable doses of temozolomide treatment [10]. UBE3C‐mediated degradation of the tumour suppressor protein membrane‐bound protein 7 promotes glioma progression [11]. UBE3C promotes lung tumourigenesis by facilitating the degradation of the p53 cofactor protein AHNAK and enhances the transcription of stem cell‐related genes in non‐small cell lung cancer [12]. HECTD1 plays a key role in regulating cell adhesion and motility [8] and is involved in cancer migration through ubiquitinating PIPKIc90 [13]. Recently, the deletion of HECTD1 was reported to increase Snail expression, which in turn leads to the downregulation of E‐cadherin expression [14, 15]. HECTD2 could be decreased by miR‐221 in prostate cancer and promotes the growth of prostate cancer cells [16]. HECTD2 also facilitates renal cancer proliferation, apoptosis and migration [17]. HECTD3 ubiquitinates caspase‐8 and prevents its recruitment to the death‐inducing signalling complex for self‐cleavage and subsequent activation [18]. Long non‐coding RNA HEIH could promote cell proliferation, migration and invasion by increasing the expression of HECTD4 in cholangiocarcinoma [19]. HACE1 mediates the development of autophagy in cardiac disease and inhibits the cell invasion of colorectal cancer through ubiquitination [20, 21].

The ‘Other’ subfamily of HECT E3s is closely related to cancer. However, their roles in HCC were not uncovered comprehensively. We aim to explore the possible roles of the ‘Other’ subfamily of HECT E3s members in HCC by utilising the bioinformatic databases and partial functional experiments to evaluate their expression patterns, prognostic values and involvements in immune infiltration. Our study could provide novel therapeutic targets for HCC.

2. MATERIALS AND METHODS

2.1. Data acquisition and processing

The RNAseq data of HCC samples were downloaded from a UCSC Xena web server (https://xenabrowser.net/datapages/). The UCSC Xena database is a data analysis and visualisation platform developed by the University of California, Santa Cruz (UCSC). It has a rich diversity of data types. These data come from various public datasets such as The Cancer Genome Atlas Project (TCGA), Genotype‐Tissue Expression Project (GTEx), International Cancer Genome Consortium (ICGC) etc., covering a wide range of biological samples and tissue types. We downloaded from UCSC the TCGA Liver Cancer (LIHC) containing 19 datasets, which include gene expression, copy number, and related phenotypic information from high‐throughput sequencing of 423 samples. Wilcoxon Rank Sum Test was adopted to evaluate the ‘Other’ subfamily of the HECT E3s expression level between different groups using the ‘ggplot2’ and ‘reshape2’ package of R 4.1.3 software. (https://www.r‐project.org/).

2.2. Survival analysis

The biological relevance of the ‘Other’ subfamily of HECT E3s expression to clinical prognosis was evaluated by the Kaplan–Meier (KM) plotter and R software. KM Plotter (http://kmplot.com/analysis/) is a publicly available database for assessing the relationship between gene expression and survival trend in multiple cancers [22]. The cutoff value for low expression versus high expression was set as the value automatically selected by the best cutoff model. The survival data downloaded from the TCGA was statistically analysed by the R package ‘survivor’ and visualised by the R package ‘survminer’. The log‐rank test was used to calculate the p value.

2.3. UALCAN analysis

UALCAN (http://ualcan.path.uab.edu) is an easy resource to use an interactive web portal for an in‐depth analysis of TCGA gene expression data [23]. In this study, UALCAN was used to analyse the associations of the ‘Other’ subfamily of HECT E3s members with clinicopathologic parameters, such as gender, age, cancer stage, tumour grade, nodal metastasis status and TP53 mutation status in HCC.

2.4. Functional enrichment analysis

To further elucidate the pathological function of the ‘Other’ subfamily of HECT E3s members in HCC, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the TCGA‐LIHC data from the UCSC Xena database. According to the median expression level of each gene, we dithe samples into high expression group and low expression group for enrichment analysis. In addition, co‐expressed genes for the ‘Other’ subfamily of HECT E3s were extracted using LinkedOmics (http://www.linkedomics.org/login.php), and relevant enrichment analysis was also performed. Heatmaps and volcano maps were drawn using the ‘pheatmap’, ‘gpubr’, and ‘ggscatter’ packages in R software. The visualisation of GO and KEGG analysis results is performed with the ‘org.Hs. for example,.db’, ‘enrichplot’ and ‘clusterProfiler’ packages.

2.5. Immune infiltration analysis

TIMER2.0 (http://timer.comp‐genomics.org) is an online server capable of analysing and displaying tumour immunity and its association with other tumour molecular and clinical features [24]. It is used to view the difference in gene expression levels in different cancer types as well as immune infiltrating cell levels for interested genes.

2.6. Cell lines and RNA interference

QSG‐7701, HCCLM3, Focus and Huh7 cells were purchased from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences. All cell lines were maintained in DMEM (HyClone, Logan, Utah, USA) supplemented with 10% foetal bovine serum and 1% penicillin—streptomycin within a humidified incubator containing 5% CO2 at 37°C.

Three different small‐interfering RNA (siRNA) sequences targeting HECTD2 were designed and synthesised by GenePharma (Shanghai, China). The siRNAs were transfected with RiboBio transfection reagent in accordance with the manufacturer's protocol, and subsequent experiments were conducted after transfection for 24 h. The sequences of siRNA‐targeted HECTD2 were listed as follows: si‐HECTD2#1: forward: 5′‐GGAUGCAUCAUCCGAATT‐3’; reverse: 5′‐UUCGGAUGAUGAUGCAUCCTT‐3’; si‐HECTD2#2: forward: 5′‐CCUGCAAAGCCUGAAGAAUTT‐3’; reverse: 5′‐ AUUCUUCAGGCUUUGCAGGTT‐3’; si‐HECTD2#3: forward: 5′‐ CCAUUGGUUUAGCAGCUUUTT‐3’; reverse: 5′‐ AAAGCUGCUAAACCAAUGGTT‐3’.

2.7. Quantitative real‐time fluorescence PCR

cDNA was synthesised using Fastking gDNA Dispelling RT SuperMix (TIANGEN, Beijing, China). Quantitative real‐time fluorescence PCR (qRT‐PCR) was performed with SuperReal PreMix Plus (TIANGEN) according to the instructions. The relative expression of RNAs was calculated by the 2−ΔΔCt quantification method. qRT‐PCR amplification was performed using the following primers: HECTD2: forward: 5′‐AGTTCACCTGCACATCTTGTTT‐3’; reverse: 5′‐GCCTTCATTTCGGATGATGATGC‐3’. The primers were designed and synthesised by GenePharma.

2.8. Western blotting analysis

The total protein was extracted from the HCC cells with a radio immunoprecipitation assay lysis buffer and protease inhibitor (Beyotime, Shanghai, China). Protein concentration was assessed by the BCA assay kit (Beyotime). Protein was separated in a 10% SDS polyacrylamide gel electrophoresis and transferred to a PVDF membrane. The membrane was blocked with QuickBlock™ Blocking Buffer (Beyotime) and incubated with diluted primary antibody overnight at 4°C. The primary antibody against HECTD2 was purchased from Abcam (Cambridge, UK) and the primary antibody against GAPDH was purchased from Servicebio (Wuhan, China). Then, the membrane was treated with horseradish peroxidase (HRP)‐labelled goat anti‐rabbit or anti‐mouse IgG antibodies (Cell Signalling Technology, Danvers, MA, USA) at room temperature for 2 h. The protein bands were detected using the SuperKine ECL Detection Reagent (Abbkine, Wuhan, China) and Bio‐Rad (Hercules, CA, USA) gel scanning.

2.9. Cell proliferation assays

Cell counting kit‐8 (CCK‐8) and 5‐ethynyl‐2′‐deoxyuridine staining (EdU) assays were used to detect the proliferation ability of HCC cells. For the CCK‐8 assay, a total of 2 × 103 transfected cells were uniformly seeded in 96‐well plates and cultured for 24, 48, 72 and 96 h. CCK‐8 reagent (BestBio, Shanghai, China) was added into each well for 2 h incubation. The absorbance at a wavelength of 450 nm was detected on an enzyme immune‐assay analyser (Bio‐Rad). EdU assay was performed using the EdU detection kit (RiboBio). The EdU incorporation rate was calculated as the ratio of the number of EdU‐incorporated cells to the number of Hoechst 33,342‐staining cells.

2.10. Colony formation assays

Huh7 and Focus cells were grown in a 6‐well plate at 37°C for 2 weeks, followed by fixation in methanol and staining in a 0.1% crystal violet solution for 15 min before colony counting.

2.11. Cell migration and invasion assays

The migratory capacity of HCC cells was evaluated by wound healing and transwell assays. The transfected HCC cells were spread evenly across the plate and incubated in 6 wells until 100% confluence. Then, a sterile 100 mL pipette tip was used to scratch the cell monolayer and produce a clear wound. Cells were washed with PBS to remove floating cells and incubated with fresh serum‐containing medium for 48 h. Images of the cells were obtained at 0, 24, and 48 h using a light microscope system. The scratch area was measured using ImageJ software, and the cell migration rate was determined using the following formula: cell migration rate (%) = (1–scratch area/original scratch area) × 100%. For the migration assay, transfected cells (2 × 104) were then suspended in 200 mL of serum‐free medium and inoculated in the upper chamber, while the lower chamber was added with cell culture medium containing 10% FBS. For the invasion assay, the upper chamber was pre‐covered with a layer of Matrigel gel (YB356234, BD Biosciences, USA), placed in a 24‐well plate and dried overnight, and the same number of cells was added to the upper chamber. After 48 h incubation, the cells were fixed with 4% formaldehyde and stained with 0.1% crystal violet staining solution for 15 min. The migrated or invaded cells were counted using a light microscope.

2.12. Statistical analysis

GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, USA) was used to perform the statistical analysis. Data are shown as mean ± SEM of the mean. The two‐sided student's t test was used to analyse the differences between groups. The differences of the ‘Other’ subfamily of HECT E3s gene expression levels between tumour and non‐tumour specimens were evaluated by the Wilcoxon test. The Chi‐square test was adopted to analyse the association of HECTD2, HECTD3, and HACE1 expression with clinicopathological features. The Kaplan–Meier curve with log‐rank test was used to compare the survival outcome. Pearson's correlation was preformed to analyse the correlation between levels. p < 0.05 was considered statistically significant.

3. RESULTS

3.1. Expression pattern of the ‘Other’ subfamily of HECT E3s in pan‐cancer

We examined the expression pattern of the ‘Other’ subfamily of HECT E3s in various cancer types using TCGA data. The results showed that the mRNA levels of the ‘Other’ subfamily of HECT E3s (UBE3A, UBE3B, UBE3C, HECTD1, HECTD2, HECTD3 and HACE1) were dysregulated in pan‐cancer (Figures 1a–g). Additionally, their expression levels were upregulated in HCC compared with normal liver tissues. Results from the UALCAN database confirmed the aberrant expression pattern of the ‘Other’ subfamily of HECT E3s in HCC. Except for HECTD4, the expression of all seven genes was elevated and statistically significant in HCC (Figures S1a–g). In our paired data analysis using R language, we showed that the expressions of UBE3A, UBE3B, UBE3C, HECTD2, HECTD3, HECTD4 and HACE1, except for HECTD1, were significantly overexpressed in HCC (Figures S2a–h). Collectively, these data suggested that the ‘Other’ subfamily of HECT E3s is aberrantly expressed in HCC.

FIGURE 1.

FIGURE 1

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The mRNA expression of the ‘Other’ subfamily of HECT E3 ubiquitin ligases (E3s) in pan‐cancer by the TIMER database. (a) The expression level of UBE3A in pan‐cancer. (b) The expression level of HECTD1 in pan‐cancer. (c) The expression level of UBE3B in pan‐cancer. (d) The expression level of HECTD2 in pan‐cancer. (e) The expression level of UBE3C in pan‐cancer. (f) The expression level of HECTD3 in pan‐cancer. (g) The expression level of HACE1 in pan‐cancer. *p < 0.05, **p < 0.01, ***p < 0.001.

3.2. Prognostic value of the ‘Other’ subfamily of HECT E3s in HCC

The prognostic significance of the ‘Other’ subfamily of HECT E3s was assessed by the KM plotter database. The results revealed that the low UBE3A expression was associated with shorter overall survival (OS) and progression‐free survival (PFS), and high levels of HECTD2, HECTD3 and HACE1 were significantly correlated with unfavourable OS and PFS (Figures 2a,b). However, the expressions of UBE3B and UBE3C have no significant association with OS and PFS. The low expression of HECTD1 was also only related to shorter OS in HCC patients (Figures S3a,b). In addition, the survival data of HECTD2, HECTD3 and HACE1 from the TCGA database showed similar results (Figure 2c), but the survival analysis of the remaining five genes (UBE3A, UBE3B, UBE3C, HECTD1 and HECTD4) in the TCGA database showed no obviously significance (Figure S3c). These results suggested that increased HECTD2, HECTD3 and HACE1 indicated poor prognosis in HCC.

FIGURE 2.

FIGURE 2

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The prognostic value of the ‘Other’ subfamily of HECT E3s in hepatocellular carcinoma (HCC). (a) Overall survival (OS) analysis of the ‘Other’ subfamily of HECT E3s (UBE3A, HACE1, HECTD2 and HECTD3) by Kaplan–Meier Plotter. (b) Progression‐free survival (PFS) analysis of the ‘Other’ subfamily of HECT E3s (UBE3A, HACE1, HECTD2 and HECTD3) by Kaplan–Meier Plotter. (c) The prognostic value of the ‘Other’ HECT subfamily of E3s (HECTD2, HECTD3 and HACE1) in TCGA dataset by R software.

3.3. Association of HECTD2, HECTD3 and HACE1 expression with clinicopathological features in HCC

We selected HECTD2, HECTD3 and HACE1 to analyse the relationship between their expression levels and clinicopathological parameters including gender, age, tumour stage, tumour grade, lymph node metastasis, and TP53 gene mutation in HCC. As shown in Figure 3a, the expression level of HECTD2 in HCC was correlated with gender, age, and cancer stage. The HECTD2 expression level was significantly higher in stage 1, stage 2 and stage 3 cancer tissues compared to normal tissues. Concerning tumour grade, the upregulation of HECTD2 expression was observed in grade 1, grade 2, grade 3, and grade 4 tumours, and the HECTD2 expression increased as the pathological grade increased. Additionally, the expression levels were significantly higher in TP53 mutants than in the TP53 wild type. However, the relationship between the high and low expression of HETCD2 and lymph node metastasis in HCC patients was not significant (Figure 3a). Similarly, the expression levels of HECTD3 and HACE1 in HCC were remarkably correlated with clinicopathological features including gender, age, tumour stage and tumour grade in HCC patients (Figures 3b,c). These results implied that the expression of these genes plays an important role in the progression of HCC, providing targeted guidance for the treatment of patients.

FIGURE 3.

FIGURE 3

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Association of the ‘Other’ subfamily of HECT E3s expression with the clinicopathological features of HCC patients by the UALCAN database. (a) The association of HECTD2 expression with the clinicopathological features of HCC patients. (b) The association of HECTD3 expression with the clinicopathological features of HCC patients. (c) The association of HACE1 expression with the clinicopathological features of HCC patients. ***p < 0.001, ns not significant.

3.4. Functional enrichment analysis of the ‘Other’ subfamily of HECT E3s and co‐expressed genes in HCC

GO and KEGG analysis on these genes were performed. Go analysis showed that the three genes (HECTD2, HECTD3 and HACE1) were mainly involved in proteasome‐mediated protein ubiquitination and were closely related to the activities of various ubiquitin protein transferases (Figure 4a). HECTD2 may be involved in the regulation of the Notch signalling pathway and is also associated with the differentiation of type 1 T helper (Th1) cells, type 2T helper (Th2) cells and the cell cycle (Figure 4b). HECTD3 are mostly enriched in the regulation of several important signalling pathways, such as the MAPK signalling pathway and the Ras signalling pathway (Figure 4c). HACE1 is possibly involved in inositol phosphate metabolism, platinum drug resistance and Hippo signalling pathway (Figure S4a).

FIGURE 4.

FIGURE 4

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Functional enrichment analysis of the ‘Other’ subfamily of HECT E3s and their co‐expressed genes in HCC. (a) The Gene Ontology (GO) enrichment of the ‘Other’ subfamily of HECT E3s. (b) The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment of HECTD2. (c) The KEGG pathway enrichment of HECTD3. (d) A volcano plot of HECTD2, HECTD3 and HACE1 and their co‐expressed genes in HCC. (e) The top 50 genes which have a positive correlation with HECTD2, HECTD3 and HACE1 are visualised in a heatmap. (f) The top 50 genes which have a negative correlation with HECTD2, HECTD3 and HACE1 are visualised in a heatmap.

Since these genes belong to the same subfamily, we tried to obtain more information about the function of the family through their identical co‐expressed genes. The co‐expressed genes of these three genes were obtained from the Linkedomics database, and the volcano map and heatmap of these co‐expressed genes in HCC were plotted, the top 50 genes were shown to be positively or negatively associated (Figure 4d–f). After that, we took intersections of all the co‐expressed genes of these three genes and obtained 2617 genes (Figure 5a). GO and KEGG analysis showed that these co‐expressed genes have a significant correlation with multiple major pathways. Specifically, the KEGG pathway mainly participated in Coronavirus disease, ubiquitin mediated proteolysis, PD‐L1 expression and PD‐1 checkpoint pathway in cancer (Figure 5b). GO functional analysis included biological process (BP), cell composition (CC) and molecular function (MF) [25]. For BP, the genes were largely enriched in response to oxidative stress, RNA splicing, nucleocytoplasmic transport and rRNA processing. For CC, these co‐expressed genes were mostly concentrated on ribosomes, including cytoplasmic ribosomes and ribosomal subunits. For MF, the genes were involved in ATP‐dependent activity acting on DNA and RNA polymerase binding (Figure 5c). Then, we also used the TIMER database to analyse the correlation between these genes. We found that the expression of each member was positively correlated, and HECTD2 and HACE1 have the strongest correlation (Figure 5d). According to the above results, we speculated that these genes can cooperate with co‐expressed genes to participate in the occurrence and development of HCC and the immune response to HCC by regulating certain signalling pathways. The exact mechanism still needs to be explored in more experiments.

FIGURE 5.

FIGURE 5

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Functional enrichment analysis of co‐expressed genes of the ‘Other’ subfamily of HECT E3s. (a) Intersection of co‐expressed genes of HECTD2, HECTD3 and HACE1. (b) GO analysis of intersecting 2617 genes. (c) KEGG enrichment of intersecting 2617 genes. (d) The expression of each of the ‘Other’ subfamily of HECT E3s gene (HECTD2, HECTD3 and HACE1) is significantly positively correlated.

3.5. Relationship between the expression of the ‘Other’ subfamily of HECT E3s and immune infiltration level in HCC

We further explored the relationship between the expression of the above genes (HECTD2, HECTD3 and HACE1) and the level of tumour‐infiltrating lymphocytes (TILs) by using the TIMER database. The findings showed that upregulation of HECTD2 and HACE1 expression was associated with increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells, whereas the upregulation of HECTD3 expression was related to all immune cell infiltrations except B cells. Specifically, HECTD2 was significantly associated with the infiltration levels of CD4+ T cells and macrophages, while HECTD3 and HACE1 were markedly correlated with the infiltration levels of macrophages and neutrophils (Figures 6a–c). We found that the expression levels of these genes also had a significant positive correlation with the infiltration levels of myeloid‐derived immunosuppressive cells (MDSC) and regulatory T cells (Tregs) (Figures 6d–f). Additionally, the expression levels of HECTD2 and HACE1 also correlated with tumour mutational burden (TMB) in HCC (Figures 6g,h), but there is no significant correlation between the expression of HECTD3 and TMB in HCC (Figure S4b). Next, to further understand the TCGA‐LIHC cohort of the above three genes in terms of their expression in relation to 22 immune cell types, we quantified the expression of relevant tumour immune infiltrating cells by using single sample Gene Set Enrichment Analysis. In the HECTD2 and HACE1 high expression group, the infiltration levels of CD4+ T cell, memory B cells, and Th2 cells were higher than those in the low expression group, while the expression of CD8+ T cells was higher in the low expression group (Figures 7a,b). Dendritic cells, NK cells and Type 17T helper cells were all higher in the HECTD3 high expression group (Figure 7c). The results from the TIMER database were further validated and presented in a heatmap using the CIBERSORT algorithm (Figure S5). These results revealed that these three members of the ‘Other’ subfamily of HECT E3s were closely related to TILs and may play a key role in regulating the immune microenvironment of HCC. The three genes may be involved in regulating the progression and metastasis of HCC as some immune component.

FIGURE 6.

FIGURE 6

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Relationships between the ‘Other’ subfamily of HECT E3s gene expression and immune cell infiltration level of HCC. (a) Upregulation of the HECTD2 expression was associated with increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. (b) Upregulation of HECTD3 expression was associated with increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. (c) Upregulation of HACE1 expression was associated with increased infiltration of CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. (d) Correlations between HECTD2 with myeloid‐derived immunosuppressive cells (MDSC) and T cell regulatory (Tregs) in HCC. (e) Correlations between HECTD3 with MDSC and Tregs in HCC. (f) Correlations between HACE1 with MDSC and Tregs in HCC. (g) Relationship of HECTD2 expression with tumour mutational burden (TMB) in pan‐cancer level. (h) Relationship of HACE1 expression with TMB in pan‐cancer level. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 7.

FIGURE 7

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Correlation analysis between HECTD2, HECTD3 and HACE1 gene expression and immune cell infiltration level of HCC. (a) The relationship between high and low expression groups of HECTD2 and the level of immune cell infiltration. (b) The relationship between high and low expression groups of HACE1 and the level of immune cell infiltration. (c) The relationship between high and low expression groups of HECTD3 and the level of immune cell infiltration. *p < 0.05, **p < 0.01, ***p < 0.001, ns not significant.

3.6. Correlation analysis between the expression of the ‘Other’ subfamily of HECT E3s and the expression of immune checkpoints molecules in HCC

We analysed the correlation between the expression of these genes and immune checkpoint molecules expression in different immune cells. We found that the expression of the three genes in HCC was positively correlated with the expression of marker genes in many immune infiltrating cells, such as B cells, Tumour‐associated macrophage (TAM) cells, Tregs, dendritic cells, Th1 cells and Th2 cells. Specifically, HECTD2 expression showed significant correlation with marker genes expression in B cells, TAM cells, and Tregs (Table 1). The expression of HECTD3 was visibly correlated with marker genes expression in TAM, M1 macrophages, M2 macrophages, Tregs and Th1 cells (Table S1). The relation of HACE1 with these marker genes was reflected in almost all immune infiltrating cells except NK cells and T cell exhaustion, demonstrating a strong relation between HACE1 and the marker genes expression (Table S2). The infiltration abundance of TAM cells was significantly correlated with the expression of CCL2 and IL‐10, indicating the possible involvement in the process of tumour immunosuppression. In general, T cell depletion is an important factor in the effectiveness of the immune checkpoint blockade [26]. We also found that the expression of HECTD2, HECTD3 and HACE1 markedly correlated with the expression of Tregs and T cell marker genes, which are FOXP3, CCR8, STAT5B, TGFB1 and TIM‐3. The above results further confirmed that the ‘Other’ subfamily of HECT E3s genes were remarkably related to the immune infiltration level in HCC, indicating that HECTD2, HECTD3 and HACE1 expressions play a crucial role in immune escape in HCC.

TABLE 1.

Correlation analysis between the HECTD2 expression level and the expression of immune checkpoints molecules in HCC.

Description Gene markers LIHC
None Purity
Cor p Cor P
B cell CD19 0.220 *** 0.267 ***
CD79A 0.176 ** 0.271 ***
T cell (general) CD3D 0.099 0.114 0.179 **
CD3E 0.173 ** 0.288 ***
CD2 0.154 ** 0.266 ***
TAM CCL2 0.173 *** 0.266 ***
CD68 0.216 *** 0.288 ***
IL10 0.266 *** 0.354 ***
M1 macrophage NOS2 0.134 0.025 0.138 0.029
IRF5 0.433 *** 0.439 ***
COX2 0.308 *** 0.433 ***
M2 macrophage CD163 0.151 * 0.235 ***
VSIG4 0.116 0.087 0.210 **
MS4A4A 0.138 * 0.232 **
Natural killer cell KIR2DL1 0.029 0.758 0.025 0.834
KIR2DL3 0.160 * 0.190 **
KIR2DL4 0.096 0.171 0.124 0.080
KIR3DL1 0.090 0.197 0.121 0.089
KIR3DL2 0.068 0.355 0.110 0.127
KIR3DL3 0.031 0.741 0.012 0.922
KIR2DS4 0.116 0.087 0.118 0.094
Treg FOXP3 0.214 ** 0.239 ***
CCR8 0.472 *** 0.556 ***
STAT5B 0.626 *** 0.618 ***
TGFβ 0.317 *** 0.405 ***
T cell exhaustion PD‐1 (PDCD1) 0.265 *** 0.338 ***
CTLA4 0.216 ** 0.301 ***
LAG3 0.097 0.110 0.134 *
TIM‐3 0.247 *** 0.370 ***
GZMB 0.034 0.611 0.068 0.312
Dendritic cell HLA‐DPB1 0.135 ** 0.292 ***
HLA‐DQB1 0.041 * 0.223 **
HLA‐DRA 0.196 0.533 0.117 0.059
HLA‐DPA1 0.197 ** 0.292 ***
CD1C 0.242 *** 0.317 ***
BDCA‐4 0.547 *** 0.574 ***
CD11c 0.327 *** 0.423 ***
Th1 TBX21 0.127 0.032 0.205 **
STAT4 0.255 *** 0.315 ***
STAT1 0.416 *** 0.460 ***
IFN‐γ 0.116 0.049 0.175 **
TNF 0.279 *** 0.367 ***
Th2 GATA3 0.244 *** 0.368 ***
STAT6 0.383 *** 0.364 ***
STAT5A 0.343 *** 0.397 ***
IL13 0.111 0.060 0.120 0.051
Tfh BCL6 0.470 *** 0.461 ***
IL21 0.091 0.135 0.113 0.066
Th17 STAT3 0.394 *** 0.431 ***
IL17A 0.183 ** 0.171 **
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*p < 0.05, **p < 0.01, ***p < 0.001.

3.7. HECTD2 was highly expressed in HCC cells

We have previously showed that the upregulation of HECTD2, HECTD3 and HACE1 expression has contact with poor prognosis in HCC. To further validate the results of bioinformatics analysis, we selected HECTD2 for further study. We respectively detected the expression of HECTD2 in HCC cell lines (QSG‐7701, HCCLM3, Focus and Huh7) by qRT‐PCR and western blotting (Figures 8a–b). The results identified that the mRNA as well as protein expression levels of HECTD2 were significantly higher in the three HCC cell lines compared with normal hepatocyte line QSG‐7701.

FIGURE 8.

FIGURE 8

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HECTD2 knockdown suppresses the proliferation, migration and invasion of Huh7 cells. (a) HECTD2 expression was investigated in HCC cell lines (HCCLM3, Focus and Huh7) and the normal human liver cell line QSG‐7701 using qRT‐PCR. (b) HECTD2 expression was examined in HCC cell lines (HCCLM3, Focus and Huh7) and the normal human liver cell line QSG‐7701 by western blotting. (c) The expression of HECTD2 was knocked down using three siRNAs in Huh7 cells. The knockdown efficiency was confirmed by qRT‐PCR. (d) Western blotting was performed to confirm the knockdown of HECTD2 in Huh7 cells. (e) Colony formation assays demonstrated the clone numbers in Huh7 cells with HECTD2 knockdown. (f) Proliferation of Huh7 cells after HECTD2 knockdown was detected by CCK‐8 assays. (g) Cell proliferative ability was examined by EdU assays. (h–i) Transwell assays were conducted to examine the effects of HECTD2 knockdown on Huh7 cell migration and invasion. (j) Wound healing assays were performed to assess the migration ability of Huh7 cells with HECTD2 knockdown. **p < 0.01, ***p < 0.001, ****p < 0.0001.

3.8. HECTD2 significantly promoted the proliferation, migration and invasion of HCC cells

To investigate the potential function of HECTD2 in HCC cells, we performed knockdown and overexpression of HECTD2 in Huh7 and Focus cells, respectively, based on the validation results in the cell lines. qRT‐PCR assays and western blotting were performed to verify the transfection efficiency of HECTD2 in Huh7 cells. The results showed that HECTD2 could be efficiently interfered with by si‐HECTD2#1 and si‐HECTD2#3 (Figure 8c,d). We selected si‐HECTD2#1 and si‐HECTD2#3 for subsequent functional studies. The effect of HECTD2 on HCC cell proliferation was examined using cloning formation, CCK‐8 and EdU staining assays. Colony formation assays validated that the proliferation of Huh7 cells in the knockdown HECTD2 group was significantly inhibited (Figure 8e). Growth curves from the CCK‐8 assays showed that the proliferation of HCC cells transfected with si‐HECTD2 was significantly inhibited compared to those transfected with si‐NC (Figure 8f). The results of the EdU assays indicated that silencing of HECTD2 significantly decreased the proliferative ability in HCC cells compared to the control (Figure 8g). In addition, the role of HECTD2 in cell migration and invasion was assessed by transwell and wound healing assays. Transwell assays were conducted to assess the effect of HECTD2 on HCC cell motility. As demonstrated in Figure 8h,i, we detected fewer migrated and invaded cells when using HECTD2‐silenced Huh7 cells. Moreover, the migration rate of transfected Huh7 cells was significantly induced compared to transfection with si‐NC after 24 and 48 h of performing the scratch (Figure 8j). These results manifested that silencing HECTD2 could inhibit the proliferation, migration and invasion of HCC cells in vitro, and the exact mechanism needs to be explored and verified by further experiments.

The overexpression plasmid of HECTD2 was transfected in Focus cells, and the transfection efficiency was verified by qRT‐PCR and western blotting (Figure 9a,b). The results of colony formation, CCK‐8 and EdU assays showed that HECTD2 overexpression facilitated the proliferation ability of HCC cells (Figure 9c–e). Transwell and wound healing assays showed that the migration and invasion ability of cells in the HECTD2 overexpression group was dramatically higher than those in the control group (Figure 9f,g).

FIGURE 9.

FIGURE 9

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HECTD2 overexpression promotes proliferation, migration and invasion of Focus cells. (a) The overexpression efficiency of HECTD2 in Focus cells was determined by qRT‐PCR. (b) The overexpression efficiency of HECTD2 in Focus cells was determined using western blotting. (c) Colony formation assays showed the clone numbers in Focus cells with HECTD2 overexpression. (d) Proliferation of Focus cells with HECTD2 overexpression was detected by CCK‐8 assays. (e) EdU assays for examining the effect of HECTD2 overexpression on Focus cell proliferation. (f) Migration and invasion of Focus cells with HECTD2 overexpression were examined by transwell assays. (g) Wound healing assays were conducted to examine the effects of HECTD2 overexpression on Focus cell migration. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

4. DISCUSSION

Despite the tremendous breakthroughs that have been made in early screening and drug treatment over the past decade, the prognosis of HCC patients remains unsatisfactory [27]. Therefore, it is important to explore the molecular pathogenesis and novel biomarkers of HCC for enhancing the diagnosis and prognosis of this disease. The ‘Other’ subfamily of HECT E3s family lacks a WW or RLD domain and has a different kind of N‐terminal domain compared to the ‘NEDD4’ subfamily and the ‘HERC’ subfamily [28]. This diversity may confer selectivity and specificity for E3‐substrate and E2‐E3 interactions during ubiquitination. It has been previously shown that viruses or pathogens can hijack the activity of the ‘Other’ subfamily of HECT E3s to escape the host immune response, while mutations in the ‘Other’ subfamily of HECT E3s are also associated with tumour progression, making it an attractive candidate for therapeutic intervention [8]. In recent years, there have been significant advances in the study of ubiquitinated family genes in cancer. Svilar et al. have demonstrated that the abnormal expression of UBE3B and UBE3C mediates glioma progression [11, 29]. Duhamel S et al. showed that HECTD1 promotes epithelial‐mesenchymal transition process in cancer [14, 15]. According to a study by Sun T et al., the downregulation of HECTD2 enhances prostate cancer cell growth [16]. Previous study has reported that the abnormal expression of HECTD4 in oesophageal and cholangiocarcinoma also had different effects on the proliferation, migration and invasion of cancer cells [19]. However, their functions in HCC are still obscure. Based on the data of TCGA and the experimental results, our study comprehensively explored the biological functions of this family members in HCC and identified that the genes were related to the prognosis of HCC, providing important ideas for clinically relevant targeted therapies.

In the current study, based on the data in the TIMER database and TCGA, we found that seven members of the ‘Other’ subfamily of HECT E3s were differentially expressed in various cancers including HCC. Subsequently, using KM survival analysis, we detected that high expression levels of HECTD2, HECTD3 and HACE1 were visibly related to unfavourable OS and PFS in HCC and were also significantly correlated with clinicopathological characteristics of HCC patients, such as age, histological grade, and tumour stage. These results inferred that the three genes play an important role in the progression and prognosis of HCC. We then explored the relationship between the expression of several genes related to the prognosis tumour immune microenvironment and tumour immune cell infiltration. Notably, we selected one of these genes for a series of experiments to identify its differential expression in HCC cells and further revealed that this gene has a carcinogenic effect in HCC cells and enhances HCC progression. To our knowledge, this is the first study of the biological functions of the ‘Other’ subfamily of HECT E3s in HCC.

Tumour microenvironment (TME), a complex biological ecosystem for cancer cells to survive and develop, refers to the surrounding circumstances of cancer cells, including cellular and non‐cellular components [30]. Cellular components could enhance tumour resistance by recruiting and secreting a variety of protective cytokines, while non‐cellular components can mediate drug resistance by establishing physical barriers and affecting tumour cell growth and metabolites [31]. Several studies have shown that the level of immune cell infiltration in the TME is related to tumour progression and prognosis [32, 33].

Currently, considerable research studies have confirmed the close relationship between immune cells and malignant progression of HCC. Cytotoxic T lymphocytes (CTLs) are predominantly CD8+ T cells. IFN‐γ produced by CD8+ T cells can increase antigen presentation and directly kill tumour cells in the tumour microenvironment [34, 35]. Tregs suppresses the immune response by inhibiting the CD8+ T cell effector and directly promotes tumour escape through multiple contact‐dependent and non‐contact mechanisms and tend to a poor prognosis in HCC [36, 37]. Tumour‐associated macrophages in the HCC immune microenvironment can be divided into tumour‐suppressing M1 types and tumour‐promoting M2 types. M1 type expresses high levels of IL‐12. M2 type is characterised by the production of multiple cytokines and high levels of PD‐L1, promoting extracellular matrix remodelling, angiogenesis, epithelial‐mesenchymal transition and immune system suppression [38]. MDSCs are immature myeloid cells that inhibit CTLs and NK cells by producing various enzymes, reactive oxygen species, TGF‐β, and IL‐10, inducing Tregs to suppress innate and adaptive immune responses [39]. DCs can induce T cell anergy or Tregs by expressing the ligands of checkpoint pathways such as CTLA‐4 or TIM3 and concomitant down‐regulation of the stimulatory signals, which in turn inhibit CTLs responses via IL‐10 [40].

Our study found that the expression levels of HECTD2, HECTD3 and HACE1 showed a significant positive correlation with the infiltration levels of CD4+ T cell, macrophages, neutrophils, and dendritic cells but a negative correlation with tumour purity. A large number of immunosuppressive cells, such as MDSC and Tregs, are also present in TME at the same time. They significantly inhibit the infiltration and function of CTLs, leading to continued tumour growth. We demonstrated that the expression levels of the three genes also have a significant positive association with the infiltration levels of MDSC and Tregs. In addition, it has been shown that the infiltration of immunosuppressive cells in tumours is also an important driver of tumour resistance to immune checkpoint blockade (ICB) therapy [41, 42]. This means that the abnormally increased expression levels of HECTD2, HECTD3 and HACE1 in HCC may contribute to tumour growth by suppressing the function of anti‐tumour T cells and may also lead to resistance to ICB therapy. For immunotherapy, the variability in the generation of immune responses across patients can be attributed to many factors. TMB was known as a serious factor in addition to TME, and high TMB was significantly associated with more T cell recognition and better clinical outcomes [43, 44]. The expression levels of the three genes we focused on also correlated with TMB except HECTD3, confirming further the possible involvement of these three genes in immune escape from tumours or in drug resistance to immunotherapy.

The immune checkpoint is an important immunomodulator for maintaining immune homoeostasis and preventing autoimmunity [45, 46]. It consists of stimulatory and inhibitory pathways that are important for maintaining autoimmune tolerance and regulating the type, intensity and duration of the immune response [47, 48]. We found that there was a significant correlation between the expression of HECTD2, HECTD3 and HACE1 and a large proportion of immune checkpoints in HCC. For example, the common immune checkpoints for tumour therapy include CD28, CTLA‐4, PD‐1 and PD‐L1. TAM, as an important part involved in tumour immunosuppression, also expressed the correlated factors CCL2 and IL10, which correlated dramatically with HECTD2, HACE1 and HECTD3, indicating that they may be involved in the process of tumour immunosuppression. This is consistent with the previous analysis of the tumour immunosuppressive cell infiltration level [49, 50, 51]. Additionally, TIM‐3, a key gene regulating T cell exhaustion [52], showed a strong positive correlation with the expression of the three genes, suggesting that high expression levels of the three genes play a key role in TIM‐3 mediated T cell exhaustion. It was also further confirmed that the three genes were specifically related to the infiltration level of immune cells in HCC, implying that they are possibly involved in immune escape from the TME of HCC. With the gradual deepening of tumour research and understanding, these genes are expected to be the direction and target of tumour immunotherapy.

Besides the correlation analysis from public databases, we also selected one of the genes for the experimental part of the validation. We verified the differential expression of HECTD2 in HCC cell lines and identified its role for proliferation, migration and invasion of liver cancer cells by functional assays. Further, we validated the results of bioinformatics analysis that high expression of HECTD2 could promote HCC cell proliferation, migration and invasion and is remarkably associated with the poor prognosis of HCC patients.

5. CONCLUSION

In summary, we comprehensively investigated the relevant biological roles of the ‘Other’ subfamily of HECT E3s in HCC. Our results revealed that there are three genes (HECTD2, HECTD3 and HACE1), and they might be unfavourable prognostic biomarkers for HCC. We showed that their expression levels were positively related with the abundance of infiltrating CD4+ T cells, CD8+ T cells, MDSC and Tregs in HCC. Furthermore, their expression was significantly correlated with the expression of immune checkpoint molecules (CTLA‐4, PDCD1 and TIM‐3) as well as TMB in HCC. HECTD2 knockdown inhibited HCC cells proliferation, migration and invasion, whereas HECTD2 overexpression promoted HCC progression. Future functional studies targeting the identification of additional cellular substrate articulated proteins and structural studies focusing on E3‐E2. E3‐substrate interactions will be useful for the development of therapeutic drug molecules to target the activity of the ‘Other’ subfamily of HECT E3s members in human diseases.

AUTHOR CONTRIBUTIONS

Runyu Dong, Juan Cai and Xueliang Zuo conceived and designed the study. Danping Cao, Zhixiong Wang and Peng Gao collected clinical samples. Runyu Dong, Yanna Li and Yao Fei performed the experiments. Runyu Dong, Zhixiong Wang, Menglin Zhu and Xueliang Zuo analysed the data. Runyu Dong and Juan Cai wrote the paper. Zhiqiang Chen, Juan Cai and Xueliang Zuo revised the paper. All authors read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

No potential conflicts of interest were disclosed.

Supporting information

Supporting Information S1

Click here for additional data file. (1.6MB, docx)

ACKNOWLEDGEMENTS

We would like to thank the TCGA, UALCAN, UCSC, TIMER and other public databases for providing us with relevant patient data. National Natural Science Foundation of China (Grant/Award Number: 82172651; 82002556); Natural Science Foundation of Anhui Province (Grant/Award Number: 2108085MH287); Natural Science Foundation of Anhui Education Department for Distinguished Young Scholars (Grant/Award Number: 2022AH020074; 2022AH030123); Support Plan for Outstanding Young Talents of Anhui Education Department (Grant/Award Number: gxyq2021257); Key Research and Development Project of Anhui Province (Grant/Award Number: 202104j07020019); Science and Technology Project of Wuhu City (Grant/Award Number: 2022jc52); Talent Introduction Science Foundation of Yijishan Hospital, Wannan Medical College (Grant/Award Number: YR202109; YR202110); and Provincial Quality Engineering Project of Anhui Education Department (Grant/Award Number: 2022jyxm1733, 2022jyxm1722).

Dong, R. , et al.: The ‘Other’ subfamily of HECT E3 ubiquitin ligases evaluate the tumour immune microenvironment and prognosis in patients with hepatocellular carcinoma. IET Syst. Biol. 18(1), 23–39 (2024). 10.1049/syb2.12086

Runyu Dong and Zhixiong Wang contributed equally to this work.

Contributor Information

Zhiqiang Chen, Email: zqchen@njmu.edu.cn.

Juan Cai, Email: caijuan1987@yeah.net.

Xueliang Zuo, Email: zuoxueliang0202@126.com.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supporting information. Further inquiries can be directed to the corresponding authors.

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Associated Data

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Supplementary Materials

Supporting Information S1

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Data Availability Statement

The original contributions presented in the study are included in the article/supporting information. Further inquiries can be directed to the corresponding authors.

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