# Overexpressed RING Finger 44 Correlates with Poor Prognosis in Hepatocellular Carcinoma

Authors: Yue Liu; Huasong Xia; Meng Li; Yuxin Chen; Wei Zhou; Yi Chen; Yanqing Wu
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

Introduction. Liver carcinoma is one of the most common cancers in the world and remains one of the most difficult cancers to treat. Hepatocellular cancer is the most important type of liver cancer (90%). RING Finger 44 (RNF44) is one of the E3 ligases, which play an important role in substrate recognition. It was also reported that RING Finger 44 was connected with resistant melanoma. But the relationship between RNF44 and HCC remained unknown. Materials and Methods. To analyze the role of RING Finger 44 gene in hepatocellular carcinoma, we used bioinformatics to analyze the expression level, genetic changes, immunohistochemistry, immune infiltration, diagnostic value, survival, and functional enrichment of RING Finger 44. Results. Through analyzing The Genotype-Tissue Expression and The Cancer Genome Atlas databases, we found that the expression level of RING Finger 44 was significantly increased in hepatocellular carcinoma tissues. Meanwhile, the expression of RING Finger 44 was connected with immune cell infiltration and survival time, and the expression level of RING Finger 44 could perform as a useful diagnostic and prognostic index. The functional enrichment analysis of RING Finger 44 provided some possible pathways of RING Finger 44 in hepatocellular carcinoma, which provided an important direction for the further experiments in vitro or in vivo. Conclusions. RING Finger 44, the high expression level of which predicts poor prognosis, is a potential oncogene in hepatocellular carcinoma.


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## Body

## 1. Introduction

Liver cancer is a highly malignant tumor and a common carcinoma [1, 2]. The mortality and incidence of liver cancer have been sustainable growth, posing a huge threat to people’s lives and health [3–5]. Most hepatocellular carcinoma (HCC) cases occur in developing countries, mainly in Southeast Asia and East Asia [6, 7]. China accounts for more than half of all global cases [8]. Hepatocellular carcinoma is the main type of liver malignant tumor, accounting for about 90% [9]. Hepatitis B virus (HBV) or hepatitis C virus (HCV) infection is related to the occurrence of HCC [7, 10]. The incidence of HCV-related HCC has been gradually increasing and will continue to increase in the coming decades in the United States [7]. Multiple options of HCC treatment could be chosen, while hepatectomy is still the most effective choice [11, 12]. Despite enormous progresses in the treatment and diagnosis of liver tumors over the past few decades, liver cancer remains one of the most difficult cancers to treat, and the prognosis for liver tumors remains poor [13, 14].E3 ubiquitin ligases (E3s) are a class of proteins that play an important role in substrate recognition and catalyze the transfer of ubiquitin to specific substrate proteins [15]. E3s are enzymes that determine the specificity of protein substrates [16]. E3 can bind to oncogenes or pathway proteins to play a role in tumor resistance [15]. Studies have found that RING Finger 44 (RNF44) is one of the E3 ligases. RNF44 is responsible for promoting AMPK‐α1 degradation in BRAF inhibitor resistant melanoma cells [17]. RNF44 can also play a key role in the development of osteoarthritis [18]. However, no studies have found the relationship between RNF44 and HCC.In our study, RNF44 was found to be overexpressed in hepatocellular carcinoma in The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. Immune infiltration analysis showed that RNF44 was associated with a variety of immune cells, suggesting that RNF44 may affect cellular immunity. COX survival analysis showed that RNF44 overexpression might lead to poor prognosis of HCC. In a word, our study suggested that RNF44 may be a potential oncogene for HCC.


## 2. Methods and Methods

### 2.1. Characteristics Analysis

Standardized RNA-Seq data and the corresponding clinical information were downloaded from Genotype-Tissue Expression (GTEx,https://gtexportal.org/home/index.html) and the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) database. The format of RNA-Seq was converted from fragments per kilobase per million (FPKM) to transcripts per million reads (TPM) with log2 conversion. The dividing line between the high expression set and low expression of RNF44 was the median of RNF44 expression in the HCC samples of TCGA cohort. The basic package of R software (version 3.6.3) was used to analyze the association between clinical factors and RNF44.


### 2.2. Gene Expression Analysis

In addition to using data from TCGA database, the normal tissue data was supplemented from GTEx database. The visualization of RNF44 expression in was achieved by ggplot2 package (version 3.3.3) ofR software (version 3.6.3).


### 2.3. Analysis of Genetic Variation

UCSC Xena (https://xenabrowser.net/) and cBioPortal (https://www.cbioportal.org) were used to analyze the variation characteristics of RNF44, including variation types, variation frequency, and copy number variation. The data of cBioPortal and UCSC Xena was obtained from TCGA database.


### 2.4. Immune Infiltration Analysis

We used GSVA package (version 1.34.0) ofR software (version 3.6.3) and single-sample gene set enrichment analysis (ssGSEA) algorithm to analyze the correlation between RNF44 and infiltration level of multiple immune cells. We used Spearman’s correlation coefficient to evaluate the association between RNF44 and immune infiltration.


### 2.5. ROC Curve and Survival Analysis

ROC curves were drawn using PROC packages (version 1.17.0.1) and ggplot2 packages (version 3.3.3) ofR software (version 3.6.3). In addition, the survival package (3.2-10 version) of R software (version 3.6.3) was applied to analyze the association between RNF44 expression and the survival time of HCC by COX regression model.


### 2.6. Nomogram Prediction Model

To predict the probability of survival at 1, 3, and 5 years after diagnosis for patients with HCC, we constructed a nomogram prediction model based on the multivariate Cox regression model. The Rms package (version 6.2-0) and survival package (version 3.2–10) ofR software (version 3.6.3) were used as tools for plotting the nomogram. Concordance index (C-index) was the evaluation criterion to judge the consistency of this model.


### 2.7. RNF44-Binding Proteins and RNF44-Correlated Genes Analysis

The top 19 RNF44-binding proteins were downloaded using the STRING (https://string-db.org/) tool. And we downloaded the top 100 RNF44-related genes from Gene Expression Profiling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index). The intersection of the top 19 RNF44-binding proteins and the top 100 RNF44-related genes was obtained using a Venn diagram, which was drawn by ggplot2 package (version 3.3.3) of R software (version 3.6.3). The scatter plot and heat maps of the top 5 RNF44-related genes and the integrated gene were also created by ggplot2 package (version 3.3.3) of R software (version 3.6.3).


### 2.8. Functional Enrichment Analysis

We did Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment analysis by clusterProfiler package (version 3.14.3) and org.hs.eg.db package (version 3.10.0) ofR software (version 3.6.3). The data of gene set enrichment analysis (GSEA) were from MSigDB Collections (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). GSEA enrichment analysis was done by ggplot2 package (version 3.3.3) of R software (version 3.6.3). STRING tools were used to draw protein-protein interaction (PPI) network.


### 2.9. Statistical Analysis

Shapiro–Wilk tests are used to verify the distribution of data. If the variables followed a normal distribution, they would be analyzed by the Studentt-test. If the variable did not follow a normal distribution, it would be analyzed by Wilcoxon’s test or Mann–Whitney test. And the comparison of multiple groups was analyzed using one-way ANOVA. Comparison of two groups was evaluated by Student’s t-test. Spearman correlation analysis was used to evaluate the monotonic relationship between two continuous or sequential variables. The differences between two Kaplan–Meier survival curves groups were analyzed by Log-rank test. Logistic regression was used to analyze the association between RNF44 expression and clinicopathological features. Cox regression was used to analyze the survival prognosis of HCC patients. The statistically significant index was P<0.05. SPSS 25.0 software and R 3.6.3 software were used for statistical analysis. Data visualization was done by R 3.6.3 software.


## 2.1. Characteristics Analysis

Standardized RNA-Seq data and the corresponding clinical information were downloaded from Genotype-Tissue Expression (GTEx,https://gtexportal.org/home/index.html) and the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) database. The format of RNA-Seq was converted from fragments per kilobase per million (FPKM) to transcripts per million reads (TPM) with log2 conversion. The dividing line between the high expression set and low expression of RNF44 was the median of RNF44 expression in the HCC samples of TCGA cohort. The basic package of R software (version 3.6.3) was used to analyze the association between clinical factors and RNF44.


## 2.2. Gene Expression Analysis

In addition to using data from TCGA database, the normal tissue data was supplemented from GTEx database. The visualization of RNF44 expression in was achieved by ggplot2 package (version 3.3.3) ofR software (version 3.6.3).


## 2.3. Analysis of Genetic Variation

UCSC Xena (https://xenabrowser.net/) and cBioPortal (https://www.cbioportal.org) were used to analyze the variation characteristics of RNF44, including variation types, variation frequency, and copy number variation. The data of cBioPortal and UCSC Xena was obtained from TCGA database.


## 2.4. Immune Infiltration Analysis

We used GSVA package (version 1.34.0) ofR software (version 3.6.3) and single-sample gene set enrichment analysis (ssGSEA) algorithm to analyze the correlation between RNF44 and infiltration level of multiple immune cells. We used Spearman’s correlation coefficient to evaluate the association between RNF44 and immune infiltration.


## 2.5. ROC Curve and Survival Analysis

ROC curves were drawn using PROC packages (version 1.17.0.1) and ggplot2 packages (version 3.3.3) ofR software (version 3.6.3). In addition, the survival package (3.2-10 version) of R software (version 3.6.3) was applied to analyze the association between RNF44 expression and the survival time of HCC by COX regression model.


## 2.6. Nomogram Prediction Model

To predict the probability of survival at 1, 3, and 5 years after diagnosis for patients with HCC, we constructed a nomogram prediction model based on the multivariate Cox regression model. The Rms package (version 6.2-0) and survival package (version 3.2–10) ofR software (version 3.6.3) were used as tools for plotting the nomogram. Concordance index (C-index) was the evaluation criterion to judge the consistency of this model.


## 2.7. RNF44-Binding Proteins and RNF44-Correlated Genes Analysis

The top 19 RNF44-binding proteins were downloaded using the STRING (https://string-db.org/) tool. And we downloaded the top 100 RNF44-related genes from Gene Expression Profiling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index). The intersection of the top 19 RNF44-binding proteins and the top 100 RNF44-related genes was obtained using a Venn diagram, which was drawn by ggplot2 package (version 3.3.3) of R software (version 3.6.3). The scatter plot and heat maps of the top 5 RNF44-related genes and the integrated gene were also created by ggplot2 package (version 3.3.3) of R software (version 3.6.3).


## 2.8. Functional Enrichment Analysis

We did Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment analysis by clusterProfiler package (version 3.14.3) and org.hs.eg.db package (version 3.10.0) ofR software (version 3.6.3). The data of gene set enrichment analysis (GSEA) were from MSigDB Collections (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). GSEA enrichment analysis was done by ggplot2 package (version 3.3.3) of R software (version 3.6.3). STRING tools were used to draw protein-protein interaction (PPI) network.


## 2.9. Statistical Analysis

Shapiro–Wilk tests are used to verify the distribution of data. If the variables followed a normal distribution, they would be analyzed by the Studentt-test. If the variable did not follow a normal distribution, it would be analyzed by Wilcoxon’s test or Mann–Whitney test. And the comparison of multiple groups was analyzed using one-way ANOVA. Comparison of two groups was evaluated by Student’s t-test. Spearman correlation analysis was used to evaluate the monotonic relationship between two continuous or sequential variables. The differences between two Kaplan–Meier survival curves groups were analyzed by Log-rank test. Logistic regression was used to analyze the association between RNF44 expression and clinicopathological features. Cox regression was used to analyze the survival prognosis of HCC patients. The statistically significant index was P<0.05. SPSS 25.0 software and R 3.6.3 software were used for statistical analysis. Data visualization was done by R 3.6.3 software.


## 3. Results

### 3.1. RNF44 Was Overexpressed in HCC

To validate our hypothesis, we assessed the levels of RNF44 expression, exon expression, and methylation in the TCGA cohort. As shown in Figure1(a), we found that RNF44 expression and exon expression were higher in tumors, but there was no significant difference of RNF44 methylation level between tumor tissue and normal tissue. Besides the unpaired samples statistical analysis of TCGA cohort and TCGA + GTEx cohort, the paired samples statistical analysis of TCGA cohort also showed that RNF44 was highly expressed in HCC (Figures 1(b), 1(c), and 1(e)). Baseline characteristics of HCC patients from TCGA are listed in Table 1. Immunohistochemical sections (IHC) from the Human Protein Atlas Database (HPA, https://www.proteinatlas.org/) also showed that RNF44 was overexpressed in tumor tissues of patients with hepatocellular carcinoma (Figure 1(d)). The basic information of the 6 IHC sections is listed in Table 2. These results suggested that RNF44 was overexpressed in HCC and might perform as an oncogene in HCC.Figure 1
The expression level of RNF44 in HCC. (a) The heat maps of RNF44 mRNA expression, exon expression, and methylation in HCC and corresponding normal tissues from UCSC Xena. (b) The RNF44 expression level of no paired HCC samples in TCGA dataset. (c) RNF44 expression of no HCC samples in TCGA + GTEx dataset. (d) IHC slices of RNF44 in liver samples from HPA. (e) RNF44 expression level of paired HCC samples in TCGA dataset.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)Table 1
Baseline characteristics of the RNF44 high-expression set and the low-expression set in the TCGA cohort.
CharacteristicLow expression of RNF44High expression of RNF44Total187187T StageT1100 (27%)83 (22.4%)T246 (12.4%)49 (13.2%)T333 (8.9%)47 (12.7%)T45 (1.3%)8 (2.2%)N stageN0126 (48.8%)128 (49.6%)N11 (0.4%)3 (1.2%)M stageM0135 (49.6%)133 (48.9%)M12 (0.7%)2 (0.7%)Pathologic stageStage I96 (27.4%)77 (22%)Stage II45 (12.9%)42 (12%)Stage III33 (9.4%)52 (14.9%)Stage IV3 (0.9%)2 (0.6%)Tumor statusTumor free108 (30.4%)94 (26.5%)With tumor71 (20%)82 (23.1%)GenderFemale55 (14.7%)66 (17.6%)Male132 (35.3%)121 (32.4%)RaceAsian74 (20.4%)86 (23.8%)Black or African American7 (1.9%)10 (2.8%)White98 (27.1%)87 (24%)Age≤6078 (20.9%)99 (26.5%)>60109 (29.2%)87 (23.3%)Weight (kg)≤7084 (24.3%)100 (28.9%)>7092 (26.6%)70 (20.2%)Height (cm)<17096 (28.2%)105 (30.8%)≥17076 (22.3%)64 (18.8%)BMI≤2583 (24.6%)94 (27.9%)>2588 (26.1%)72 (21.4%)Residual tumorR0165 (47.8%)162 (47%)R16 (1.7%)11 (3.2%)R21 (0.3%)0 (0%)Histologic gradeG137 (10%)18 (4.9%)G294 (25.5%)84 (22.8%)G349 (13.3%)75 (20.3%)G45 (1.4%)7 (1.9%)Adjacent hepatic tissue inflammationNone66 (27.8%)52 (21.9%)Mild51 (21.5%)50 (21.1%)Severe8 (3.4%)10 (4.2%)AFP (ng/ml)≤400123 (43.9%)92 (32.9%)>40023 (8.2%)42 (15%)Albumin (g/dl)<3.537 (12.3%)32 (10.7%)≥3.5119 (39.7%)112 (37.3%)Prothrombin time (s)≤4105 (35.4%)103 (34.7%)>448 (16.2%)41 (13.8%)Child–Pugh gradeA120 (49.8%)99 (41.1%)B12 (5%)9 (3.7%)C0 (0%)1 (0.4%)Vascular invasionNo108 (34%)100 (31.4%)Yes56 (17.6%)54 (17%)OS eventAlive129 (34.5%)115 (30.7%)Dead58 (15.5%)72 (19.3%)DSS eventAlive145 (39.6%)142 (38.8%)Dead38 (10.4%)41 (11.2%)PFI eventAlive102 (27.3%)89 (23.8%)Dead85 (22.7%)98 (26.2%)RNF44: ring finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition.Table 2
The basic information of IHC slices.
Tissue typeIDAgeGenderNormal-1242955MaleNormal-2322263FemaleNormal-3340254FemaleTumor-1276673FemaleTumor-2239952FemaleTumor-3334673Female


### 3.2. The Location Site and Variation of RNF44 in HCC

We found that RNF44 was mainly expressed in cell nucleus, as shown in Figure2(b), from HPA. The 3D morphology, common mutation sites, and basic gene information of RNFF44 were described in Figures 2(c) and 2(d). Furthermore, we found that 2.7% of RNF44 mutations were present in HCC samples, of which proliferation was the most frequent, by analyzing the molecular characteristics of RNF44 (Figure 2(a)). And after analyzing the correlation between the mRNA expression level, genetic changes, and copy number of RNF44, we found that the mutations of proliferation increased the mRNA expression level of RNF44 (Figures 2(e)–2). These results further confirmed that RNF44 was overexpressed in HCC and might play a carcinogenic role in hepatocytes.Figure 2
The location site and variation of RNF44 in HCC. (a) The genetic alteration frequency of RNF44 in HCC using the cBioPortal tool. (b) The location site of RNF44 in HCC. (c) The 3D structure of RNF44 in HCC from cBioPortal. (d) The mutation sites of RNF44 in HCC from cBioPortal. (e) The difference between the mRNA expression level and copy number of RNF44 from cBioPortal. (f) The correlations between the mRNA expression level and copy number of RNF44 from cBioPortal.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.


### 3.3. Correlation between Immune Infiltration Level of Different Immune Cells and RNF44 Expression Level

As shown in Table3 and Figure 3, among different immune cell types, the expression level of RNF44 was correlated with aDC, cytotoxic cells, DC, eosinophils, neutrophils, NK D56 bright cells, pDC, T helper cells, Tcm, TFH, Tgd and Th2 cells, and Treg infiltration level. The expression levels of T helper cells, Th2 cells, TFH, Tcm, eosinophils, NK CD56 bright cells, aDC, and macrophages were positively correlated with RNF44 expression. The expression levels of Tgd, Treg, neutrophils, cytotoxic cells, DC, and pDC were significantly negatively correlated with RNF44 expression. These results suggested that the expression level of RNF44 might be related to tumor immunity and influence tumor immunity, which might help to guide the immunotherapy of HCC. RNF44 might be a new immunotherapy target of HCC patients in the future.Table 3
Connection between the expression level of RNF44 and the immune infiltration in the tumor microenvironment.
Immune cellSpearman’s correlationP valueT helper cells0.413<0.001B cells0.0230.657CD8 T cells−0.0250.635Th2 cells0.331<0.001iDC0.0450.386Mast cells−0.0410.427TFH0.241<0.001Tcm0.223<0.001NK CD56dim cells−0.0550.286NK cells−0.0140.782Eosinophils0.211<0.001T cells−0.0140.791NK CD56bright cells0.197<0.001aDC0.1280.013Th1 cells0.0760.143Th17 cells−0.0610.239Macrophages0.1030.047Tem0.1020.050Tgd−0.1310.011Treg−0.1570.002Neutrophils−0.200<0.001Cytotoxic cells−0.304<0.001DC−0.312<0.001pDC−0.345<0.001aDC: activated dendritic cell; DC: dendritic cell; iDC: immature dendritic cell; Macrophages: Mast cells; pDC : plasmacytoid dendritic cell; Tcm :T central memory; Tem : T effector memory; Tfh : T follicular helper; Tgd : T gamma delta; and Treg: T regulatory cell.Figure 3
Correlations between the expression level of RNF44 and immune infiltration. (a) The bar graph about the immune infiltration level of different immune infiltration cells in RNF44 high set and low set. (b) The lollipop figure about the correlations between the expression level of RNF44 and immune infiltration of different immune infiltration cells. (c) The scatter diagrams about the correlations between the expression level of RNF44 and immune infiltration level of different immune infiltration cells.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)


### 3.4. The Relationship between RNF44 Expression Level and Some Clinical Features

To further understand the relationship between RNF44 and HCC, we analyzed the expression level of RNF44 and clinicopathological characteristics data from TCGA database. The patients were divided into the low expression group and the high expression group based on the median value. The results showed that age (P=0.026), weight (P=0.039), histologic grade (P=0.002), pathologic stage (P=0.021), and AFP (P=0.002) were significantly correlated with RNF44 expression. There was a part correlation with T stage (P=0.053) (Table 4). Figure 4 showed the visualization of the correlation between the above clinical features and the expression level of RNF44. These results showed that patients with younger age, lower body weight, higher histologic grade, higher tumor stage, higher pathologic stage, and higher blood AFP value were more prone to express higher RNF44. In other words, higher RNF44 expression predicted early-onset HCC, increased probability of tumor cachexia, poorer histologic grade, larger tumor size, higher pathologic stage, and higher blood AFP value. All these indicated a poor prognosis of HCC.Table 4
The correlation between RNF44 expression and clinicopathologic factors in the TCGA cohort.
CharacteristicsTotal (N)Odds ratio (OR)P valueAge (>60 vs. ≤60)3730.629 (0.417–0.946)0.026Weight (kg) (>70 vs. ≤70)3460.639 (0.417–0.976)0.039Histologic grade (G3 and 4 vs. G1 and 2)3691.950 (1.272–3.009)0.002T stage (T3 and 4 vs. T1 and 2)3711.601 (0.998–2.589)0.053Pathologic stage (stage III & IV vs. stage I & II)3501.777 (1.096–2.910)0.021N stage (N1 vs. N0)2582.953 (0.373–60.136)0.351M Stage (M1 vs. M0)2721.015 (0.120–8.560)0.988AFP (ng/ml) (>400 vs. ≤400)2802.441 (1.384–4.396)0.002Note. The stages were graded according to AJCC7. RNF44: RING Finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition.Figure 4
The correlation between RNF44 expression and clinical factors in TCGA. The correlation between RNF44 expression and (a) age, (b) weight, (c) AFP, (d) T stage, (e) histological grade, and (f) pathological stage.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)(f)


### 3.5. The Diagnostic Value of RNF44 Expression in HCC

In this study, ROC analysis was performed to explore the diagnostic value of RNF44 expression level in HCC. As shown in Figure5(a), the area under the curve (AUC) of RNF44 is 0.897, indicating a high diagnostic value in distinguishing HCC tumors from normal liver tissues.Figure 5
Diagnostic value of the expression and the survival analysis of RNF44 in HCC. (a) ROC curve for differentiating normal people and HCC patients. (b) Overall survival plot of HCC patients that are grouped by RNF44 expression level. (c) Progression-free interval plot of HCC patients that are grouped by RNF44 expression level. ((d)–(e)) Disease-free survival plot of (d) weight <70 kg and (e) Asian subtypes. ((f)–(i)) Overall survival plots of (f) BMI <25, (g) N stage =N0, (h) histologic grade = G3, and (f) Asian subtypes HCC subgroup patients. ((j)–(l)) Progression-free interval survival plots of (j) albumin (g/dl) ≥3.5, (k) T stage = T1-2, and (l) N stage = N0 and subtypes HCC subgroup patients. ∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


### 3.6. Relationship between RNF44 Expression and Survival Time in Patients with Hepatocellular Carcinoma

To analyze the association between RNF44 and survival, Kaplan–Meier analysis was performed. Univariate Cox regression analysis revealed that RNF44 expression was connected with overall survival time (P=0.027, hazard ratio [HR] = 1.48, 95% CI = 1.05–2.10, Figure 5(b)) and progression-free interval time (P=0.007, risk ratio [HR] = 1.49, 95% CI = 1.11–2.00, Figure 5(c)). Similarly, Kaplan–Meier analysis was performed in different subgroups of patients with HCC, and the results were shown in Figures 5(d)–5(l). Kaplan–Meier analysis results of different clinical subgroups of HCC also suggested that the expression of RNF44 was related to progression-free interval, disease-specific survival, and overall survival. In further Cox univariate and multivariate analyses, we found that higher expression of RNF44, higher tumor stage, higher pathologic stage, distant metastasis, and vascular invasion were correlated with poor progression-free interval (Table 5). The expression level of RNF44 was an independent risk factor for progression-free interval. This reminded us that higher expression of RNF44 predicted poor overall survival, poor disease-specific survival, and poor progression-free interval in HCC patients. Therefore, we drawn a nomogram for predicting the 1-year, 3-year, and 5-year progression-free interval possibility of patients with HCC (C-index = 0.632, 95% CI = 0.575–0.688) (Figure 6(a)). We used ROC analysis to test the diagnostic specificity of the nomogram. As shown in Figure 6(h), the area under the curve (AUC) of 1 year, 3 years, and 5 years was 0.783, 0.703, and 0.617, indicating the effective predictive value of the model.Table 5
Univariate and multivariate COX regression analysis for disease-specific survival in the TCGA cohort.
CharacteristicsTotal (N)Univariate analysisMultivariate analysisHazard ratio (95% CI)P valueHazard ratio (95% CI)P valueT stage370T1 & T2277ReferenceT3 & T4932.177 (1.590–2.980)<0.0010.372 (0.080–1.732)0.207Pathologic stage349Stage I & stage II259ReferenceStage III & stage IV902.201 (1.591–3.046)<0.0014.112 (0.871–19.421)0.074Gender373Female121ReferenceMale2520.982 (0.721–1.338)0.909Age373≤60177Reference>601960.960 (0.718–1.284)0.783BMI336≤25177Reference>251590.936 (0.689–1.272)0.673Histologic grade368G1 & G2233ReferenceG3 & G41351.152 (0.853–1.557)0.355AFP (ng/ml)279≤400215Reference>400641.045 (0.698–1.563)0.832Fibrosis Ishak score214075Reference1/2 and 3/4 and 5/61391.363 (0.911–2.039)0.132N stage258N0254ReferenceN141.370 (0.338–5.552)0.659M stage272M0268ReferenceM143.476 (1.091–11.076)0.0352.266 (0.632–8.123)0.209Weight345≤70184Reference>701611.016 (0.750–1.375)0.920Height340<170201Reference≥1701391.252 (0.919–1.706)0.154Albumin (g/dl)299<3 569Reference≥3 52300.911 (0.618–1.341)0.636Adjacent hepatic tissue inflammation236None118ReferenceMild & severe1181.238 (0.867–1.768)0.241Prothrombin time296≤4207Reference>4891.100 (0.785–1.541)0.581Child–Pugh grade240A218ReferenceB & C221.395 (0.765–2.545)0.277Vascular invasion317No208ReferenceYes1091.676 (1.196–2.348)0.0031.332 (0.885–2.006)0.169RNF44373Low187ReferenceHigh1861.511 (1.129–2.022)0.0061.483 (1.003–2.195)0.049Note. The stages were graded according to AJCC7. RNF44: RING Finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition. Bold means statistically significant.Figure 6
A nomogram about progression-free interval. (a) A nomogram for predicting the 1-year, 3-year, and 5-year progression-free interval survival probability of HCC patients. (b) ROC curves of the nomogram.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)


### 3.7. Functional Enrichment Analysis of RNF44

To further explore the carcinogenic mechanism of RNF44, we used the STRING tools to download the top 19 RNF44-binding proteins and the top 100 RNF44-related genes from GEPIA2. Then, we made enrichment analysis. The protein-protein interaction (PPI) network between the top 19 RNF44 binding proteins is shown in Figure7(a). Among the top 100 RNF44-related genes, MAML1 (P<0.01, R = 0.859), KDM3B1 (P<0.01, R = 0.832), TCERG1 (P<0.01, R = 0.832), UIMC1 (P<0.01, R = 0.794), and CPSF6 (P<0.01, R = 0.817) had the strongest correlation with RNF44 (Figures 7(b)–7(d)) and showed a significant positive correlation with the expression level of RNF44. FAF2 (P<0.01, R = 0.775, Figures 7(b)– 7(d)) was the common gene of the top 19 proteins and the top 100 genes (Figure 7(c)). Then, we combined these genes and proteins for the following GO and KEGG analyses. GO enrichment analysis includes three functional groups, that is, molecular functions, cellular components, and biological processes (Figures 8(a)–8(c)). Figure 8(d) shows the results of KEGG analysis. Figure 7(e) shows the GO and KEGG enrichment interaction network. These results showed that RNF44 and other related genes were mainly related to regulation of mRNA splicing and protein complex.Figure 7
RNF44-related gene and RNF44-binding proteins enrichment analysis. (a) Protein–protein interaction network of RNF44. (b) Heat map about the top 5 RNF44-related gene enrichment. (c) A Venn diagram about intersection analysis of the top 50 proteins and the top 100 genes. (d) The scatter diagrams about the top 5 RNF44-related gene and the intersection gene of Venn.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)Figure 8
GO and KEGG enrichment analyses of RNF44. ((a)–(c)) GO enrichment analyses of RNF44. (d) KEGG enrichment analyses of RNF44. (e) GO and KEGG enrichment interactive network.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)To further explore the signaling pathway activated by RNF44 in HCC, we performed GSEA analysis about those 118 related genes. We identified several important RNF44-related signaling pathways. In this study, we mapped the first four pathways including REACTOME_CD22_MEDIATED_BCR_REGULATION, REACTOME_FCGR_ACTIVATION, REACTOME_ANTIGEN_ACTIVATES_B_CELL_RECEPTOR_BCR_LEADING_TO_GENERATION_OF_SECOND_MESSENGERS, and REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS (Figure9). GSEA functional enrichment analysis suggested that RNF44 might be correlated with FCGR and CD22, as well as with immune function.Figure 9
GSEA enrichment analysis of RNF44.
(a)(b)(c)(d)(e)


## 3.1. RNF44 Was Overexpressed in HCC

To validate our hypothesis, we assessed the levels of RNF44 expression, exon expression, and methylation in the TCGA cohort. As shown in Figure1(a), we found that RNF44 expression and exon expression were higher in tumors, but there was no significant difference of RNF44 methylation level between tumor tissue and normal tissue. Besides the unpaired samples statistical analysis of TCGA cohort and TCGA + GTEx cohort, the paired samples statistical analysis of TCGA cohort also showed that RNF44 was highly expressed in HCC (Figures 1(b), 1(c), and 1(e)). Baseline characteristics of HCC patients from TCGA are listed in Table 1. Immunohistochemical sections (IHC) from the Human Protein Atlas Database (HPA, https://www.proteinatlas.org/) also showed that RNF44 was overexpressed in tumor tissues of patients with hepatocellular carcinoma (Figure 1(d)). The basic information of the 6 IHC sections is listed in Table 2. These results suggested that RNF44 was overexpressed in HCC and might perform as an oncogene in HCC.Figure 1
The expression level of RNF44 in HCC. (a) The heat maps of RNF44 mRNA expression, exon expression, and methylation in HCC and corresponding normal tissues from UCSC Xena. (b) The RNF44 expression level of no paired HCC samples in TCGA dataset. (c) RNF44 expression of no HCC samples in TCGA + GTEx dataset. (d) IHC slices of RNF44 in liver samples from HPA. (e) RNF44 expression level of paired HCC samples in TCGA dataset.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)Table 1
Baseline characteristics of the RNF44 high-expression set and the low-expression set in the TCGA cohort.
CharacteristicLow expression of RNF44High expression of RNF44Total187187T StageT1100 (27%)83 (22.4%)T246 (12.4%)49 (13.2%)T333 (8.9%)47 (12.7%)T45 (1.3%)8 (2.2%)N stageN0126 (48.8%)128 (49.6%)N11 (0.4%)3 (1.2%)M stageM0135 (49.6%)133 (48.9%)M12 (0.7%)2 (0.7%)Pathologic stageStage I96 (27.4%)77 (22%)Stage II45 (12.9%)42 (12%)Stage III33 (9.4%)52 (14.9%)Stage IV3 (0.9%)2 (0.6%)Tumor statusTumor free108 (30.4%)94 (26.5%)With tumor71 (20%)82 (23.1%)GenderFemale55 (14.7%)66 (17.6%)Male132 (35.3%)121 (32.4%)RaceAsian74 (20.4%)86 (23.8%)Black or African American7 (1.9%)10 (2.8%)White98 (27.1%)87 (24%)Age≤6078 (20.9%)99 (26.5%)>60109 (29.2%)87 (23.3%)Weight (kg)≤7084 (24.3%)100 (28.9%)>7092 (26.6%)70 (20.2%)Height (cm)<17096 (28.2%)105 (30.8%)≥17076 (22.3%)64 (18.8%)BMI≤2583 (24.6%)94 (27.9%)>2588 (26.1%)72 (21.4%)Residual tumorR0165 (47.8%)162 (47%)R16 (1.7%)11 (3.2%)R21 (0.3%)0 (0%)Histologic gradeG137 (10%)18 (4.9%)G294 (25.5%)84 (22.8%)G349 (13.3%)75 (20.3%)G45 (1.4%)7 (1.9%)Adjacent hepatic tissue inflammationNone66 (27.8%)52 (21.9%)Mild51 (21.5%)50 (21.1%)Severe8 (3.4%)10 (4.2%)AFP (ng/ml)≤400123 (43.9%)92 (32.9%)>40023 (8.2%)42 (15%)Albumin (g/dl)<3.537 (12.3%)32 (10.7%)≥3.5119 (39.7%)112 (37.3%)Prothrombin time (s)≤4105 (35.4%)103 (34.7%)>448 (16.2%)41 (13.8%)Child–Pugh gradeA120 (49.8%)99 (41.1%)B12 (5%)9 (3.7%)C0 (0%)1 (0.4%)Vascular invasionNo108 (34%)100 (31.4%)Yes56 (17.6%)54 (17%)OS eventAlive129 (34.5%)115 (30.7%)Dead58 (15.5%)72 (19.3%)DSS eventAlive145 (39.6%)142 (38.8%)Dead38 (10.4%)41 (11.2%)PFI eventAlive102 (27.3%)89 (23.8%)Dead85 (22.7%)98 (26.2%)RNF44: ring finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition.Table 2
The basic information of IHC slices.
Tissue typeIDAgeGenderNormal-1242955MaleNormal-2322263FemaleNormal-3340254FemaleTumor-1276673FemaleTumor-2239952FemaleTumor-3334673Female


## 3.2. The Location Site and Variation of RNF44 in HCC

We found that RNF44 was mainly expressed in cell nucleus, as shown in Figure2(b), from HPA. The 3D morphology, common mutation sites, and basic gene information of RNFF44 were described in Figures 2(c) and 2(d). Furthermore, we found that 2.7% of RNF44 mutations were present in HCC samples, of which proliferation was the most frequent, by analyzing the molecular characteristics of RNF44 (Figure 2(a)). And after analyzing the correlation between the mRNA expression level, genetic changes, and copy number of RNF44, we found that the mutations of proliferation increased the mRNA expression level of RNF44 (Figures 2(e)–2). These results further confirmed that RNF44 was overexpressed in HCC and might play a carcinogenic role in hepatocytes.Figure 2
The location site and variation of RNF44 in HCC. (a) The genetic alteration frequency of RNF44 in HCC using the cBioPortal tool. (b) The location site of RNF44 in HCC. (c) The 3D structure of RNF44 in HCC from cBioPortal. (d) The mutation sites of RNF44 in HCC from cBioPortal. (e) The difference between the mRNA expression level and copy number of RNF44 from cBioPortal. (f) The correlations between the mRNA expression level and copy number of RNF44 from cBioPortal.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.


## 3.3. Correlation between Immune Infiltration Level of Different Immune Cells and RNF44 Expression Level

As shown in Table3 and Figure 3, among different immune cell types, the expression level of RNF44 was correlated with aDC, cytotoxic cells, DC, eosinophils, neutrophils, NK D56 bright cells, pDC, T helper cells, Tcm, TFH, Tgd and Th2 cells, and Treg infiltration level. The expression levels of T helper cells, Th2 cells, TFH, Tcm, eosinophils, NK CD56 bright cells, aDC, and macrophages were positively correlated with RNF44 expression. The expression levels of Tgd, Treg, neutrophils, cytotoxic cells, DC, and pDC were significantly negatively correlated with RNF44 expression. These results suggested that the expression level of RNF44 might be related to tumor immunity and influence tumor immunity, which might help to guide the immunotherapy of HCC. RNF44 might be a new immunotherapy target of HCC patients in the future.Table 3
Connection between the expression level of RNF44 and the immune infiltration in the tumor microenvironment.
Immune cellSpearman’s correlationP valueT helper cells0.413<0.001B cells0.0230.657CD8 T cells−0.0250.635Th2 cells0.331<0.001iDC0.0450.386Mast cells−0.0410.427TFH0.241<0.001Tcm0.223<0.001NK CD56dim cells−0.0550.286NK cells−0.0140.782Eosinophils0.211<0.001T cells−0.0140.791NK CD56bright cells0.197<0.001aDC0.1280.013Th1 cells0.0760.143Th17 cells−0.0610.239Macrophages0.1030.047Tem0.1020.050Tgd−0.1310.011Treg−0.1570.002Neutrophils−0.200<0.001Cytotoxic cells−0.304<0.001DC−0.312<0.001pDC−0.345<0.001aDC: activated dendritic cell; DC: dendritic cell; iDC: immature dendritic cell; Macrophages: Mast cells; pDC : plasmacytoid dendritic cell; Tcm :T central memory; Tem : T effector memory; Tfh : T follicular helper; Tgd : T gamma delta; and Treg: T regulatory cell.Figure 3
Correlations between the expression level of RNF44 and immune infiltration. (a) The bar graph about the immune infiltration level of different immune infiltration cells in RNF44 high set and low set. (b) The lollipop figure about the correlations between the expression level of RNF44 and immune infiltration of different immune infiltration cells. (c) The scatter diagrams about the correlations between the expression level of RNF44 and immune infiltration level of different immune infiltration cells.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)


## 3.4. The Relationship between RNF44 Expression Level and Some Clinical Features

To further understand the relationship between RNF44 and HCC, we analyzed the expression level of RNF44 and clinicopathological characteristics data from TCGA database. The patients were divided into the low expression group and the high expression group based on the median value. The results showed that age (P=0.026), weight (P=0.039), histologic grade (P=0.002), pathologic stage (P=0.021), and AFP (P=0.002) were significantly correlated with RNF44 expression. There was a part correlation with T stage (P=0.053) (Table 4). Figure 4 showed the visualization of the correlation between the above clinical features and the expression level of RNF44. These results showed that patients with younger age, lower body weight, higher histologic grade, higher tumor stage, higher pathologic stage, and higher blood AFP value were more prone to express higher RNF44. In other words, higher RNF44 expression predicted early-onset HCC, increased probability of tumor cachexia, poorer histologic grade, larger tumor size, higher pathologic stage, and higher blood AFP value. All these indicated a poor prognosis of HCC.Table 4
The correlation between RNF44 expression and clinicopathologic factors in the TCGA cohort.
CharacteristicsTotal (N)Odds ratio (OR)P valueAge (>60 vs. ≤60)3730.629 (0.417–0.946)0.026Weight (kg) (>70 vs. ≤70)3460.639 (0.417–0.976)0.039Histologic grade (G3 and 4 vs. G1 and 2)3691.950 (1.272–3.009)0.002T stage (T3 and 4 vs. T1 and 2)3711.601 (0.998–2.589)0.053Pathologic stage (stage III & IV vs. stage I & II)3501.777 (1.096–2.910)0.021N stage (N1 vs. N0)2582.953 (0.373–60.136)0.351M Stage (M1 vs. M0)2721.015 (0.120–8.560)0.988AFP (ng/ml) (>400 vs. ≤400)2802.441 (1.384–4.396)0.002Note. The stages were graded according to AJCC7. RNF44: RING Finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition.Figure 4
The correlation between RNF44 expression and clinical factors in TCGA. The correlation between RNF44 expression and (a) age, (b) weight, (c) AFP, (d) T stage, (e) histological grade, and (f) pathological stage.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)(f)


## 3.5. The Diagnostic Value of RNF44 Expression in HCC

In this study, ROC analysis was performed to explore the diagnostic value of RNF44 expression level in HCC. As shown in Figure5(a), the area under the curve (AUC) of RNF44 is 0.897, indicating a high diagnostic value in distinguishing HCC tumors from normal liver tissues.Figure 5
Diagnostic value of the expression and the survival analysis of RNF44 in HCC. (a) ROC curve for differentiating normal people and HCC patients. (b) Overall survival plot of HCC patients that are grouped by RNF44 expression level. (c) Progression-free interval plot of HCC patients that are grouped by RNF44 expression level. ((d)–(e)) Disease-free survival plot of (d) weight <70 kg and (e) Asian subtypes. ((f)–(i)) Overall survival plots of (f) BMI <25, (g) N stage =N0, (h) histologic grade = G3, and (f) Asian subtypes HCC subgroup patients. ((j)–(l)) Progression-free interval survival plots of (j) albumin (g/dl) ≥3.5, (k) T stage = T1-2, and (l) N stage = N0 and subtypes HCC subgroup patients. ∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 3.6. Relationship between RNF44 Expression and Survival Time in Patients with Hepatocellular Carcinoma

To analyze the association between RNF44 and survival, Kaplan–Meier analysis was performed. Univariate Cox regression analysis revealed that RNF44 expression was connected with overall survival time (P=0.027, hazard ratio [HR] = 1.48, 95% CI = 1.05–2.10, Figure 5(b)) and progression-free interval time (P=0.007, risk ratio [HR] = 1.49, 95% CI = 1.11–2.00, Figure 5(c)). Similarly, Kaplan–Meier analysis was performed in different subgroups of patients with HCC, and the results were shown in Figures 5(d)–5(l). Kaplan–Meier analysis results of different clinical subgroups of HCC also suggested that the expression of RNF44 was related to progression-free interval, disease-specific survival, and overall survival. In further Cox univariate and multivariate analyses, we found that higher expression of RNF44, higher tumor stage, higher pathologic stage, distant metastasis, and vascular invasion were correlated with poor progression-free interval (Table 5). The expression level of RNF44 was an independent risk factor for progression-free interval. This reminded us that higher expression of RNF44 predicted poor overall survival, poor disease-specific survival, and poor progression-free interval in HCC patients. Therefore, we drawn a nomogram for predicting the 1-year, 3-year, and 5-year progression-free interval possibility of patients with HCC (C-index = 0.632, 95% CI = 0.575–0.688) (Figure 6(a)). We used ROC analysis to test the diagnostic specificity of the nomogram. As shown in Figure 6(h), the area under the curve (AUC) of 1 year, 3 years, and 5 years was 0.783, 0.703, and 0.617, indicating the effective predictive value of the model.Table 5
Univariate and multivariate COX regression analysis for disease-specific survival in the TCGA cohort.
CharacteristicsTotal (N)Univariate analysisMultivariate analysisHazard ratio (95% CI)P valueHazard ratio (95% CI)P valueT stage370T1 & T2277ReferenceT3 & T4932.177 (1.590–2.980)<0.0010.372 (0.080–1.732)0.207Pathologic stage349Stage I & stage II259ReferenceStage III & stage IV902.201 (1.591–3.046)<0.0014.112 (0.871–19.421)0.074Gender373Female121ReferenceMale2520.982 (0.721–1.338)0.909Age373≤60177Reference>601960.960 (0.718–1.284)0.783BMI336≤25177Reference>251590.936 (0.689–1.272)0.673Histologic grade368G1 & G2233ReferenceG3 & G41351.152 (0.853–1.557)0.355AFP (ng/ml)279≤400215Reference>400641.045 (0.698–1.563)0.832Fibrosis Ishak score214075Reference1/2 and 3/4 and 5/61391.363 (0.911–2.039)0.132N stage258N0254ReferenceN141.370 (0.338–5.552)0.659M stage272M0268ReferenceM143.476 (1.091–11.076)0.0352.266 (0.632–8.123)0.209Weight345≤70184Reference>701611.016 (0.750–1.375)0.920Height340<170201Reference≥1701391.252 (0.919–1.706)0.154Albumin (g/dl)299<3 569Reference≥3 52300.911 (0.618–1.341)0.636Adjacent hepatic tissue inflammation236None118ReferenceMild & severe1181.238 (0.867–1.768)0.241Prothrombin time296≤4207Reference>4891.100 (0.785–1.541)0.581Child–Pugh grade240A218ReferenceB & C221.395 (0.765–2.545)0.277Vascular invasion317No208ReferenceYes1091.676 (1.196–2.348)0.0031.332 (0.885–2.006)0.169RNF44373Low187ReferenceHigh1861.511 (1.129–2.022)0.0061.483 (1.003–2.195)0.049Note. The stages were graded according to AJCC7. RNF44: RING Finger protein 44; AJCC7: American Joint Committee on Cancer 7th edition. Bold means statistically significant.Figure 6
A nomogram about progression-free interval. (a) A nomogram for predicting the 1-year, 3-year, and 5-year progression-free interval survival probability of HCC patients. (b) ROC curves of the nomogram.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)


## 3.7. Functional Enrichment Analysis of RNF44

To further explore the carcinogenic mechanism of RNF44, we used the STRING tools to download the top 19 RNF44-binding proteins and the top 100 RNF44-related genes from GEPIA2. Then, we made enrichment analysis. The protein-protein interaction (PPI) network between the top 19 RNF44 binding proteins is shown in Figure7(a). Among the top 100 RNF44-related genes, MAML1 (P<0.01, R = 0.859), KDM3B1 (P<0.01, R = 0.832), TCERG1 (P<0.01, R = 0.832), UIMC1 (P<0.01, R = 0.794), and CPSF6 (P<0.01, R = 0.817) had the strongest correlation with RNF44 (Figures 7(b)–7(d)) and showed a significant positive correlation with the expression level of RNF44. FAF2 (P<0.01, R = 0.775, Figures 7(b)– 7(d)) was the common gene of the top 19 proteins and the top 100 genes (Figure 7(c)). Then, we combined these genes and proteins for the following GO and KEGG analyses. GO enrichment analysis includes three functional groups, that is, molecular functions, cellular components, and biological processes (Figures 8(a)–8(c)). Figure 8(d) shows the results of KEGG analysis. Figure 7(e) shows the GO and KEGG enrichment interaction network. These results showed that RNF44 and other related genes were mainly related to regulation of mRNA splicing and protein complex.Figure 7
RNF44-related gene and RNF44-binding proteins enrichment analysis. (a) Protein–protein interaction network of RNF44. (b) Heat map about the top 5 RNF44-related gene enrichment. (c) A Venn diagram about intersection analysis of the top 50 proteins and the top 100 genes. (d) The scatter diagrams about the top 5 RNF44-related gene and the intersection gene of Venn.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)(e)Figure 8
GO and KEGG enrichment analyses of RNF44. ((a)–(c)) GO enrichment analyses of RNF44. (d) KEGG enrichment analyses of RNF44. (e) GO and KEGG enrichment interactive network.∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, and ∗∗∗∗P<0.0001.
(a)(b)(c)(d)To further explore the signaling pathway activated by RNF44 in HCC, we performed GSEA analysis about those 118 related genes. We identified several important RNF44-related signaling pathways. In this study, we mapped the first four pathways including REACTOME_CD22_MEDIATED_BCR_REGULATION, REACTOME_FCGR_ACTIVATION, REACTOME_ANTIGEN_ACTIVATES_B_CELL_RECEPTOR_BCR_LEADING_TO_GENERATION_OF_SECOND_MESSENGERS, and REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS (Figure9). GSEA functional enrichment analysis suggested that RNF44 might be correlated with FCGR and CD22, as well as with immune function.Figure 9
GSEA enrichment analysis of RNF44.
(a)(b)(c)(d)(e)


## 4. Discussion

Liver cancer is a common cancer, showing a high degree of malignancy and posing a serious threat to people’s health and life. Liver patients in China account for more than half of the total number of global cases [1–5]. Among liver cancer subtypes, HCC is the most important subtype, accounting for about 90% [9]. Although there was a great progress in the diagnosis and treatment of liver tumors. The treatment of liver cancer includes surgery, chemotherapy, radiotherapy, TACE, immunotherapy, targeted therapy liver, and liver transplantation. Liver cancer is still one of the most difficult cancers to treat, and the prognosis of liver tumors is still poor [11–14].E3s are a class of specific enzymes that play an important role in substrate recognition and catalyzing ubiquitin transfer to specific substrate proteins [15, 16] and also play a certain role in tumor drug resistance [15]. RING Finger 44 is one of the E3 ligases. Therefore, there might be a certain correlation with the occurrence and development of HCC. In this study, we evaluated RNF44 expression in the TCGA cohort and found that RNF44 expression and exon expression were higher in tumors. Amplification, among many variants, was the main change in HCC, further confirming high expression of RNF44 in HCC. Immune infiltration analysis showed that RNF44 was associated with a variety of immune cells, suggesting that RNF44 might affect cellular immunity, which was consistent with our initial conjecture that RNF44 might affect tumor drug resistance by affecting E3 ubiquitin ligase. This can guide the immunotherapy of HCC. To further understand the relationship between RNF44 and HCC, we also analyzed the association between clinical features and RNF44. In the TCGA cohort, higher expression of RNF44 might predict earlier hepatocellular carcinoma, increased probability of tumor cachexia, poorer histologic grade, larger tumor size, higher pathologic stage, and higher blood AFP values. These clinicopathological features were suggestive of poorer disease survival outcomes. Further COX survival analysis also showed that RNF44 overexpression was an independent risk factor for progression-free interval of HCC.Taken together, our results suggested that RNF44 might be a potential oncogene for HCC. Based on COX regression analysis, we constructed an exploratory nomogram to predict the 1-year, 3-year, and 5-year progression-free interval survival probability of HCC patients. ROC curve analysis indicated that the nomogram had a good predictive value, which might provide some help for the clinical diagnosis and treatment of HCC patients in the future. Subsequent GO, KEGG, and GSEA functional enrichment analysis suggested that RNF44 might be involved in RNA binding and protein complex processes; RNF44 might be correlated with FCGR and CD22 genes; and RNF44 might be associated with immune phagocytosis [19]. High RNF44 expression also relates to high infiltration level of NK and macrophages in hepatocellular carcinoma; NK markers such as CD56 also show significant correlations with RNF44 expression. These results further reveal that there is a strong relationship between RNF44 and NK cells infiltration. These had certain guiding significance for further basic experiments. Despite our meaningful findings, this study also showed some limitations. On the one hand, a sufficient number of local validation groups should be used to validate the analysis results of the common database. On the other hand, there was a lack of in vivo and in vitro experiments to probe the mechanism of RNF44 in the development of HCC.In conclusion, this study demonstrated that the expression of RNF44 was significantly elevated in HCC and was correlated with the degree of immune invasion of some immune cells, as well as the prognosis of hepatocellular carcinoma. The results of this study provided a new potential marker and target for HCC therapy.



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