Search for


TEXT SIZE

search for



CrossRef (0)
C4orf47 is a Novel Prognostic Biomarker and Correlates with Infiltrating Immune Cells in Hepatocellular Carcinoma
Biomed Sci Letters 2023;29:11-25
Published online March 31, 2023;  https://doi.org/10.15616/BSL.2023.29.1.11
© 2023 The Korean Society For Biomedical Laboratory Sciences.

Hye-Ran Kim1,* , Choong Won Seo1,* , Sang Jun Han2,†,* and Jongwan Kim1,†,*

1Department of Biomedical Laboratory Science, Dong-Eui Institute of Technology, Busan 47230, Korea
2Department of Biotechnology, College of Fisheries Sciences, Pukyong National University, Busan 48513, Korea
Correspondence to: Jongwan Kim. Department of Biomedical Laboratory Science, Dong-Eui Institute of Technology, 54 Yangji-ro, Busanjin-gu, Busan 47230, Korea.
Tel: +82-51-860-3525, Fax: +82-51-860-3150, e-mail: dahyun@dit.ac.kr
Sang Jun Han. Department of Biotechnology, College of Fisheries Sciences, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Korea.
Tel: +82-51-629-5862, Fax: +82-51-629-5863, e-mail: sjhan@pknu.ac.kr
*Professor.
Received November 14, 2022; Revised March 24, 2023; Accepted March 27, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
In hepatocellular carcinoma (HCC), chromosome 4 open-reading frame 47 (C4orf47) has not been so far investigated for its prognostic value or association with infiltrating immune cells. We performed bioinformatics analysis on HCC data and analyzed the data using online databases such as TIMER, UALCAN, Kaplan-Meier plotter, LinkedOmics, and GEPIA2. We found that C4orf47 expression in HCC was higher compared to normal tissues. High C4orf47 expression was associated with a worse prognosis in HCC. The correlation between C4orf47 and infiltrating immune cells is positively associated with CD4+T cells, B cells, neutrophils, macrophages, and dendritic cells in HCC. Moreover, high C4orf47 expression was correlated with a poor prognosis of infiltrating immune cells. Analysis of C4orf47 gene co-expression networks revealed that 12501 genes were positively correlated with C4orf47, whereas 7200 genes were negatively correlated. The positively related genes of C4orf47 are associated with a high hazard ratio in different types of cancer, including HCC. Regarding the biological functions of C4orf47 gene, it mainly regulates RNA metabolic process, DNA replication, and cell cycle. The C4orf47 gene may play a prognostic role by regulating the global transcriptome process in HCC. Our findings demonstrate that high C4orf47 expression correlates with poor prognosis and tumor-infiltrating immune cells in HCC. We suggest that C4orf47 is a novel prognostic biomarker and potential immune therapeutic target for HCC.
Keywords : Hepatocellular carcinoma, Prognosis, Immune cell, C4orf47
INTRODUCTION

Liver cancer is considered cancer with a high mortality rate (Bray et al., 2018). More than 600,000 people worldwide are known to die from liver cancer every year (Wang et al., 2020). 90% of all liver cancers are Hepatocellular carcinoma (HCC) (Feng et al., 2019). HCC is correlated with various risk factors such as hepatitis B and C virus, heavy alcohol intake, and type 2 diabetes (Gomes et al., 2013; Singal and El-Serag, 2015). Liver cancer treatment methods are nor effective for patients and often have a poor prognosis (Gu et al., 2020). In addition, obtaining a clear diagnosis during the early stages of HCC is difficult. Most patients are diagnosed with late-stage HCC, and its prognosis is poor. The 5-year HCC survival rate is known to be about 3~5% (Yu, 2016). Therefore, early diagnosis is a necessary part of improving patient survival. Although the understanding of HCC molecular pathogenesis has recently improved leading to the development of new approved therapies, treatment for advanced stages is still limited. Thus, novel target identification is an important research direction for liver cancer treatment. Also, prognostic biomarkers are of great significance for the treatment of patients with HCC.

The immune system plays an important part in controlling cancer progression (Gentles et al., 2015; Schreiber et al., 2011). The tumor immune microenvironment (TIME) and tumor-infiltrating immune cells (TIICs) are topics of interest that play an important role in cancer studies (Cabrita et al., 2020; Liu et al., 2019). The TIME is associated to the clinical outcomes of immunotherapy (Binnewies et al., 2018; Tekpli et al., 2019). The TIME of HCC comprises various innate and adaptive immune cells (Soo et al., 2018). It plays an important role in HCC tumor progression and therefore it serves as a prognostic factor for patients with this condition (Fernandez-Cruz et al., 1988). The TIME, characterized by a specific profile of TIICs, is correlated with the development of cancer (Balch et al., 1990; Laz훱r et al., 2018). TIICs in particular have been used to predict clinical outcomes of cancer treatments (Bense et al., 2016; Zhang et al., 2019). Indeed, various data have shown that TIICs play an important role as prognostic markers (Caruso et al., 2002). Therefore, TIICs may play a significant role in the treatment, and prognosis of HCC.

Chromosome 4 is the fourth largest chromosome, encoding 757 proteins. This chromosome contains disease-related marker genes, including genes related to diseases with a genetic component such as cancer (including HCC) as well as aging and conditions related to the cardiac, metabolic, and immune systems (Chen et al., 2013). However, many discovered genes still have unknown functions and are denoted as chromosome 4 open reading frame genes (C4orf genes). Future research aiming at characterizing these C4orf genes may potentially result in the identification of novel biomarkers for a variety of diseases. In HCC, current research efforts are focused on evaluating the use of prognostic or predictive biomarkers to improve outcomes. In particular, several studies have explored the relationship between HCC prognosis and TIIC function. Although the search for additional diagnostic, prognostic, or predictive biomarkers for HCC has been extensive, the correlation between HCC prognosis, C4orf47 gene expression and presence of TIICs has not yet been determined. As mentioned, the discovery of novel biomarkers could significantly help in the prediction of early recurrence with HCC.

In this study, we identified expression of C4orf47 and its correlation with prognosis in patients with HCC using databases. Moreover, we analyzed the correlation between C4orf47 expression and TIICs using Tumor Immune Estimation Resource (TIMER). To assess the potential biological functions and co-expression network of C4orf47, we used LinkedOmics database. The prognostic significance of C4orf47-related genes was further investigated using GEPIA2. The results indicate that C4orf47 may be used as a novel prognostic biomarker, and that it provides insights into tumor immunology in HCC.

MATERIALS AND METHODS

TIMER database Analysis

TIMER is a comprehensive resource for systematic analysis of immune infiltrates across diverse cancer types (https://cistrome.shinyapps.io/timer/) (Li et al., 2017). TIMER applies a deconvolution method (Li et al., 2016) to infer the abundance of TIICs from gene expression profiles. The TIMER database includes 10,897 samples across 32 cancer types from The Cancer Genome Atlas (TCGA) to estimate the abundance of immune infiltrates. We analyzed C4orf47 gene expression and survival rates, including clinical data (sex, race, age, and stage) in HCC. We analyzed the correlation between C4orf47 gene expression and TIICs, including B cells, CD4+ and CD8+ T cells, neutrophils, macrophages, and dendritic cells via gene modules. Gene expression levels against tumor purity are displayed in the leftmost panel (Aran et al., 2015). Moreover, we analyzed the association between prognosis and immune cell infiltration in HCC.

The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) database analysis

UALCAN (http://ualcan.path.uab.edu) uses TCGA level 3 RNA-seq and clinical data from 31 cancer types (Chandrashekar et al., 2017), allowing analysis of the relative expression of genes across tumor and normal samples, as well as in various tumor subgroups based on individual cancer stages, tumor grade, or other clinicopathological features.

KM database Analysis

KM is based on an online database (Gy철rffy et al., 2010). It can assess the effect of 54,675 genes on survival using 10,461 cancer samples and is capable of identifying the association of genes with survival in various types of cancer, including HCC. KM includes survival rates, such as overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), disease-specific survival (DSS), and clinical data (including sex, race, stage, grade, AJCC_T stage, vascular invasion, alcohol consumption, and hepatitis virus status) in patients with HCC. The correlations between C4orf47 and survival were investigated and presented with the hazard ratio (HR), 95% confidence intervals, and log rank P-value computed (L찼nczky et al., 2016).

LinkedOmics database analysis

The LinkedOmics database (http://www.linkedomics.org/admin.php) is a web-based platform for analyzing 32 TCGA cancer-associated multidimensional datasets (Vasaikar et al., 2018). C4orf47 co-expression was analyzed statistically using Pearson's correlation coefficient, presented in volcano plots, heat maps, or scatter plots. The Function module of LinkedOmics analyzes Gene Ontology (GO) biological process (GO_BP), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, kinase-target enrichment, miRNA-target enrichment, and transcription factor-target enrichment by the gene set enrichment analysis (GSEA). The rank criterion was FDR<0.05, and 500 simulations were performed.

Gene Expression Profiling Interactive Analysis version 2 (GEPIA2)

The GEPIA2 database (http://gepia.cancer-pku.cn/) is an interactive web that includes 9,736 tumors and 8,587 normal samples from TCGA and Genotype-Tissue Expression (GTEx) projects (Tang et al., 2017). GEPIA2 performs survival analyses based on gene or isoform expression levels. GEPIA2 was used to generate survival curves, including OS and DFS, based on gene expression in 33 different cancer types. The median was selected as the threshold for splitting the high- and low-expression cohorts (cut-off = 50%). GEPIA2 provides a heat map to show survival analysis results based on multiple cancer types (Tang et al., 2019). Heatmap of OS and DFS based on C4orf47 gene expression across all TCGA tumor types were obtained in the "Survival Map" module. GEPIA2 generated survival curves based on C4orf47 gene expression using the log-rank test and Mantel-Cox test.

Statistical Analysis

Gene expression data acquired from the TIMER database were analyzed using online tools. Survival curves were generated using KM plots, TIMER, and GEPIA2 online tools. Survival results are displayed as HR and P or Cox P values from a log-rank test. The log-rank test (P < 0.05) indicated the significance of survival time differences. The correlation between gene expression and immune signature score was evaluated using the TIMER database using Spearman's correlation. In this study, all data were extracted from well-known open databases, and all analyses were conducted using web tools. All results are presented as P-values from the log-rank test. Statistical significance was set at P < 0.05.

RESULTS

mRNA expression levels of C4orf47 gene in HCC and various types of cancer

The flow chart of the analysis process is shown in Fig. 1. To determine the differences in gene expression of C4orf47 between tumor and normal tissues, C4orf47 expression was analyzed in all tissue samples using the TIMER. The results indicated that expression of C4orf47 was higher in HCC than in normal tissues. Moreover, bladder urothelial carcinoma (BLCA), cholangiocarcinoma (CHOL) tumor, colon adenocarcinoma (COAD) tumor, esophageal carcinoma (ESCA) tumor, glioblastoma multiforme (GBM) tumor, head and neck squamous cell carcinoma (HNSC) tumor, kidney renal clear cell carcinoma (KIRC) tumor, kidney renal papillary cell carcinoma (KIRP) tumor, prostate adenocarcinoma (PRAD) tumor and uterine corpus endometrial carcinoma (UCEC) tumor were higher than normal tissues (Fig. 2A). Based on the above analysis, the correlation between gene expression of C4orf47 and primary tumor, tumor stage, tumor grade, and lymph node metastasis status in HCC was identified. The results showed a significant correlation between C4orf47 expression levels of and the primary tumor, tumor stage and tumor grade in HCC. Regarding nodal metastasis status, the gene expression of C4orf47 in N0 was higher than normal tissues (Fig. 2B-E). These results showed that expression of C4orf47 was upregulated in HCC.

Fig. 1. Flow chart of the analysis process.

Fig. 2. C4orf47 gene expression in various types of cancer including HCC.
(A) High or low expression of C4orf47 gene in different human tumor tissues compared with normal tissues using the TIMER database. The expression of C4orf47 gene in primary tumor (B), tumor stage (C), tumor grade (D), and lymph node metastasis status (E) compared with normal tissues using the UALCAN database.

The prognostic significance of C4orf47 gene expression in HCC

We showed whether gene expression of C4orf47 was correlated with HCC prognosis. Therefore, both C4orf47 gene expression and survival rates were evaluated in HCC using the KM and TIMER databases. The findings revealed that high gene expression of C4orf47 was associated with poorer prognosis in HCC (OS: HR = 1.76, P = 0.0013; RFS: HR = 1.45, P = 0.029; PFS: HR = 1.42, P = 0.021; DSS: HR = 1.82, P = 0.0078; Fig. 3A). In addition, high C4orf47 expression correlated with clinical features associated with a poorer OS concerning sex, race, age, and stage (Fig. 3B). Indeed, high C4orf47 expression correlated with poorer OS in male (HR = 2.23, P = 0.00031), stage I + II (HR = 1.87, P = 0.01), grade II (HR = 1.74, P = 0.04), Asians (HR = 2.31, P = 0.0051), alcohol consumption (yes: HR = 2.05, P = 0.029; None: HR = 1.67, P = 0.029), and presence of hepatitis virus (Yes: HR = 2.66, P = 0.0037). High C4orf47 gene expression also correlated with poorer RFS in female (HR = 1.91, P = 0.028), Asians (HR = 1.89, P = 0.014), and alcohol consumption (HR = 1.64, P = 0.029). High C4orf47 gene expression was correlated with poorer PFS in female (HR = 1.86, P = 0.016), stage I + II (HR = 1.49, P = 0.045), alcohol consumption (HR = 1.76, P = 0.0056), and hepatitis virus (Yes: HR = 1.77, P = 0.018). High C4orf47 gene expression was associated with poorer DSS in male (HR = 1.85, P = 0.035), stage I + II (HR = 2.27, P = 0.017), stage II (HR = 6.49, P = 0.0015), stage II + III (HR = 2.66, P = 0.0015), grade II (HR = 2.1, P = 0.03), grade III (HR = 2.8, P = 0.0057), AJCC_T II (HR = 5.88, P = 0.00064), and alcohol consumption (Yes: HR = 2.27, P = 0.029) (Table 1). These results showed that C4orf47 gene has an impact on the prognosis of HCC.

Correlation between C4orf47 gene and clinicopathological characteristics in HCC.

The clinicopathological characteristics of C4orf47 gene were analyzed using the Kaplan-Meier plotter

Clinicopathological characteristics Overall survival (n = 3,218) Relapse free survival (n = 2,809) Progression free survival (n = 3,162) Disease specific survival (n = 3,189)
N Hazard ratio P-value N Hazard ratio P-value N Hazard ratio P-value N Hazard ratio P-value
SEX
Male 246 2.23 (1.44~3.57) 0.00031 210 1.27 (0.84~1.9) 0.29 149 1.24 (0.85~1.79) 0.26 244 1.85 (1.03~3.31) 0.035
Female 118 1.14 (0.65~2.02) 0.64 106 1.91 (1.06~3.44) 0.028 121 1.86 (1.11~3.09) 0.016 118 1.63 (0.8~3.31) 0.17
STAGE
I 170 1.62 (0.87~3.03) 0.13 153 1.21 (0.68~2.16) 0.52 171 1.25 (0.73~2.14) 0.14 168 1.16 (0.43~3.08) 0.77
I+II 253 1.87 (1.15~3.03) 0.01 228 1.4 (0.91~2.16) 0.12 256 1.49 (1.01~2.2) 0.045 251 2.27 (1.13~4.53) 0.017
II 83 1.58 (0.69~3.62) 0.28 75 1.57 (0.81~3.06) 0.18 85 1.42 (0.77~2.61) 0.26 83 6.49 (1.74~24.23) 0.0015
II+III 166 1.6 (0.97~2.62) 0.62 145 1.36 (0.87~2.13) 0.17 170 1.29 (0.86~1.94) 0.22 166 2.66 (1.42~4.99) 0.0015
III 83 1.58 (0.85~2.93) 0.15 70 1.13 (0.62~2.07) 0.68 85 0.99 (0.58~1.69) 0.96 83 1.81 (0.87~3.8) 0.11
III+IV 87 1.48 (0.81~2.7) 0.2 70 1.13 (0.62~2.07) 0.68 90 1.21 (0.71~2.06) 0.49 87 1.71 (0.83~3.51) 0.14
IV 4 - - 0 - 5 - - 3 - -
GRADE
I 65 1.13 (0.32~4.08) 0.85 55 1.49 (0.44~4.66) 0.49 55 1.11 (0.41~3.0) 0.84 55 0.69 (0.08~5.54) 0.72
II 174 1.74 (1.02~2.97) 0.04 149 1.51 (0.93~2.47) 0.097 177 1.5 (0.96~2.33) 0.072 171 2.1 (1.06~4.17) 0.03
III 118 1.68 (0.92~3.07) 0.09 107 1.21 (0.71~2.07) 0.49 121 1.53 (0.92~2.55) 0.097 119 2.8 (1.31~5.99) 0.0057
IV 12 - - 11 - - 12 - - 12 - -
AJCC_T
I 180 1.7 (0.94~3.08) 0.077 160 1.27 (0.37~2.21) 0.4 181 1.29 (0.77~2.15) 0.33 178 1.35 (0.57~3.21) 0.49
II 90 1.73 (0.81~3.72) 0.15 80 1.44 (0.77~2.72) 0.25 93 1.41 (0.81~2.47) 0.23 91 5.88 (1.87~18.47) 0.00064
III 78 1.85 (0.98~3.96) 0.054 67 1.12 (0.6~2.1) 0.71 80 1.17 (0.66~2.1) 0.58 77 1.89 (0.88~4.08) 0.098
IV 13 - 6 - - 13 - - 13 - -
Vascular invasion
None 203 1.43 (0.84~2.44) 0.19 175 1.1 (0.65~1.84) 0.73 205 1.23 (0.77~1.97) 0.38 201 1.47 (0.7~3.1) 0.3
Micro 90 1.42 (0.65~3.08) 0.38 82 0.92 (0.47~1.79) 0.8 92 1.02 (0.56~1.83) 0.96 90 0.73 (0.22~2.39) 0.6
Macro 16 - - 14 - - 16 14 - -
RACE
White 181 1.25 (0.78~2.0) 0.35 147 1.22 (0.77~1.93) 0.4 184 1.37 (0.92~2.04) 0.12 179 1.51 (0.85~2.66) 0.15
Asian 154 2.31 (1.26~4.21) 0.0051 145 1.89 (1.13~3.15) 0.014 157 1.44 (0.88~2.33) 0.14 154 2.02 (0.91~4.46) 0.077
Alcohol consumption
Yes 115 2.05 (1.06~3.95) 0.029 99 1.55 (0.86~2.8) 0.14 117 1.12 (0.67~1.89) 0.66 117 2.27 (1.07~4.81) 0.029
None 202 1.67 (1.05~2.65) 0.029 183 1.64 (1.05~2.56) 0.029 205 1.76 (1.17~2.64) 0.0056 199 1.66 (0.89~3.09) 0.11
Hepatitis virus
Yes 150 2.66 (1.34~5.27) 0.0037 139 1.6 (0.95~2.69) 0.074 153 1.77 (1.1~2.86) 0.018 151 2.1 (0.88~5) 0.086
None 167 1.22 (0.78~1.91) 0.39 133 1.32 (0.8~2.17) 0.28 169 1.22 (0.79~1.89) 0.36 165 1.38 (0.79~2.42) 0.26


Fig. 3. The prognostic significance of high C4orf47 gene expression in HCC.
The prognostic significance of C4orf47 gene was analyzed using the Kaplan-Meier plotter database (A) and TIMER database (B). Overall survival, OS; Relapse free survival, RFS; progression free survival, PFS; Disease specific survival, DSS.

Correlation between C4orf47 gene and infiltrating immune cells in HCC

Our results revealed that C4orf47 genes were positively associated with infiltration levels of T(+CD4) cells (R = 0.12, P = 2.58e-02), B cells (R = 0.179, P = 8.48e-04), neutrophils (R = 0.237, P = 8.76e-06), macrophages (R = 0.139, P = 9.81e-03), and dendritic cells (R = 0.269, P = 3.75e-07) in HCC (Fig. 4A). Next, we identified whether C4orf47 was associated with prognosis and TIICs in HCC. The results revealed that high C4orf47 gene expression combined with high infiltration levels of either T(+CD4) or B cells were associated with a worse prognosis than low C4orf47 gene expression together with high T(+CD4) or B cell infiltration levels. In addition, high C4orf47 gene expression combined with high infiltration levels of either neutrophils or macrophages were associated with a worse prognosis than low C4orf47 gene expression together with low neutrophil or macrophage infiltration levels (Fig. 4B). These results suggest that high C4orf47 expression is associated with infiltrating immune cells.

Fig. 4. Correlation between C4orf47 gene and infiltrating immune cells in HCC.
(A) The correlation between C4orf47 gene and infiltrating immune cells (CD4+T cells, CD8+T cells, neutrophils, macrophages, dendritic cells, and B cells) was analyzed using the TIMER database. (B) The prognostic value between C4orf47 gene and infiltrating immune cells was analyzed using the TIMER database.

C4orf47 gene Co-expression network in HCC

12501 genes (dark red dots) were positively associated with C4orf47, and 7200 genes (dark green dots) were negatively associated (false discovery rate (FDR) < 0.01) (Fig. 5A). Heat maps were used to represent the top 50 genes that were positively associated with C4orf47 (Fig. 5B) and negatively associated with C4orf47 (Fig. 5C). We further identified the biological process categories of GO and showed that C4orf47 and its co-expression genes involved in RNA localization, ribonucleoprotein complex localization, and protein-containing complex localization, etc. (Fig. 6A). We then carried out the KEGG analysis, and the results showed that co-expressed genes were enriched in the spliceosome, cell cycle, ribosome biogenesis in eukaryotes, RNA transport, synaptic vesicle cycle, pathogenic Escherichia coli infection, phagosome, interleukin-17 signaling pathway, rheumatoid arthritis, DNA replication, and homologous recombination (Fig. 6B). C4orf47 gene showed a positive association with expression of PPFIA4 (R = 0.5159, P = 6.09e-26), SLC2A1 (R = 0.4955, P = 9.271e-24), SPATA17 (R = 0.4888, P = 4.49e-23), CA9 (R = 0.4548, P = 7.944e-20), G6PD (R = 0.4413, P = 1.224e-18), and CXCL5 (R = 0.4264, P = 2.216e-17) (Fig. 7). These results present that C4orf47 may play prognostic roles by regulating the global transcriptome process in HCC.

Fig. 5. C4orf47 gene co-expression in HCC.
C4orf47 co-expression genes were analyzed using the LinkedOmics database. (A) Highly correlated genes of C4orf47 tested by Pearson test in HCC cohort. Heat maps showing top 50 genes positively and negatively correlated with C4orf47 gene in HCC. Red indicates positively correlated genes (B) and blue indicates negatively correlated genes (C).

Fig. 6. Enriched GO functions and KEGG pathways of C4orf47 gene in HCC.
GO and KEGG analysis were analyzed using the LinkedOmics database. (A) Biological process enrichment analysis of C4orf47 co-expressed genes by gene set enrichment analysis (GSEA). (B) KEGG pathway analysis of C4orf47 co-expressed genes by GSEA.

Fig. 7. Correlation with positive-related genes of C4orf47 in HCC.
The positive-related genes of C4orf47 were analyzed using the LinkedOmics database. All of these genes were significantly correlated with prognosis of OS in HCC.

The prognostic value of C4orf47 positive-related gene in HCC

We investigated the prognosis of C4orf47-related genes in HCC using the GEPIA2 database. The findings displayed that patient with high gene expression of C4orf47 had significantly shorter survival times than those with low expression. High C4orf47 gene expression was associated with poorer prognosis in HCC (OS, HR = 2.1, P = 7.8e-0.5; DFS, HR = 1.6, P = 0.0028; Fig. 8A) Next, we analyzed survival map of C4orf47-related genes in various types of cancer including HCC. The results revealed that C4orf47-related genes were highly likely to be high-risk genes in HCC. Twenty nine genes showed a high HR (P < 0.05) for OS, and seven genes had high HR for DFS (Fig. 8B, C). In contrast, 21 of the negative genes had a low HR (P < 0.05) for OS, and 12 genes had a low HR for DFS (Fig. 8D, E). Moreover, the genes that were positively associated with C4orf47 indicated a high HR in various cancer types, and the negative genes indicated low HR in various cancer types (Fig. 9).

Fig. 8. The prognostic significance of C4orf47-related genes of in HCC.
The prognostic significance of C4orf47-related genes was analyzed using the GEPIA2 database. (A) Survival curve of C4orf47 gene in overall survival (OS) and disease-free survival (DFS). Survival map of the positive-related genes of C4orf47 in OS (B) and DFS (C). Survival map of the negative-related genes of C4orf47 in OS (D) and DFS (E). Heat map presenting the log10 (HR) of the genes in HCC. A square with bold border represents a P value < 0.05 in the survival analysis.

Fig. 9. The prognostic significance of C4orf47-related genes of in various cancers.
The prognostic significance of C4orf47-related genes was analyzed using the GEPIA2 database. Survival map of the positive-related genes of C4orf47 in OS (A) and RFS (B). Survival map of the negative-related genes of C4orf47 in OS (C) and RFS (D). Heat map presenting the log10 (HR) of the genes in various cancers. A square with bold border represents a P value < 0.05 in the survival analysis.

DISCUSSION

Liver cancer is an important cancer related to cancer worldwide (Nguyen et al., 2015). HCC, caused by chronic hepatitis and fibrosis, in common type of liver cancer (Dapito et al., 2012; Uehara et al., 2013). HCC accounts for 85~90% of total liver cancer cases (Bray et al., 2018). Due to the lack of methods for early diagnosis of the disease, HCC is diagnosed in an advanced state (Kulik and El-Serag, 2019; Wallace et al., 2015). Therefore, there is a need for biomarkers for effective diagnosis and prognosis evaluation of HCC.

The immune system plays crucial roles in regulating cancer progression and activity of immune cells can potentially promote cancer progression (Goswami et al., 2017; Munhoz and Postow, 2016; Ostrand-Rosenberg, 2008). Immunological characteristics of HCC are complex and vary due to the co-existing chronic inflammation and cirrhosis (Cariani and Missale, 2019; Lu et al., 2019). HCC influences the growth, invasion, and metastasis of tumors and the efficacy of treatment (Prieto et al., 2015). TIICs can assist in the growth of cancer cells as well as solid tumors (Bremnes et al., 2011). The density and type of TIICs is closely associated with the clinical outcome of the tumor (Angell et al., 2018; Choi et al., 2017; Hao et al., 2017). In the last few years, several studies have revealed the prognostic value of TIICs and immune molecules in HCC (Harding et al., 2019; Sun et al., 2019). This evidence showed that TIICs play an important role as prognostic markers and potential therapeutic targets. Thus, TIICs may play a significant role in the development, treatment, and HCC prognosis.

In this study, we identified the expression level of C4orf47 in HCC. Based on the TIMER database, we found that gene expression of C4orf47 was higher in various cancer include HCC but lower in KICH, LUSC, and THCA tissues than in normal adjacent tissues. Based on the UALCAN database, C4orf47 expression was higher in primary tumors, tumor stage and tumor grade in HCC. In these databases, we found prognostic correlations between C4orf47 expression in HCC. We showed that high C4orf47 expression correlated with a high HR for poor OS, RFS, PFS, and DSS. Furthermore, analysis of data from TIMER databases showed that high C4orf47 gene expression correlated with poor prognosis factors in HCC in terms of gender, race, age, and stage. Indeed, high C4orf47 gene expression correlated with clinic-pathological factors such as male sex, grade II, Asian ethnicity, stage I+II, AJCC_T II, alcohol consumption, and hepatitis virus. Taken together, these findings strongly suggest that C4orf47 can be used as a prognostic biomarker for HCC.

C4orf47 was associated with immune infiltration levels in HCC. The results demonstrated that there is a positive association between C4orf47 expression levels and tumor infiltration of immune cell such as T(CD4+) cells, B cells, neutrophils, macrophages, and dendritic cells in HCC. Moreover, we observed that high C4orf47 expression together with infiltrating immune cells associated with a poorer HCC prognosis. In particular, high gene expression of C4orf47 and high infiltration of either T cells (CD4+) or B cells showed a worse prognosis than low C4orf47 gene expression combined with high T/B cell infiltration levels. Similarly, high C4orf47 gene expression together with high infiltration of either neutrophils or macrophages were associated with a worse prognosis than both low C4orf47 gene expression and low neutrophil infiltration levels. Taken together, this finding showed that C4orf47 plays a significant role in the regulation of immune cell and tumor prognosis through tumor immunity in HCC.

In our analysis of C4orf47 gene co-expression networks, we identified that 12501 genes were positively associated with C4orf47, and 7200 genes were negatively associated. C4orf47-related genes are highly likely to be high-risk genes in HCC. Twenty-nine genes indicated a high HR in OS, and seven genes indicated a high HR in DFS. C4orf47-related negative genes indicated a low HR of 21 genes in OS, and 12 genes indicated a low HR in DFS. In addition, we showed that these genes were associated with high HR in various cancer types. Concerning the knowledge of C4orf47 gene biological function in HCC, we found that the functional effect of C4orf47 mainly includes RNA metabolic processes while it inhibits metabolic processes. Together, these results indicated that C4orf47 may play a prognostic role by regulating the global transcriptome process in HCC. In conclusion, our findings demonstrate that high C4orf47 expression correlates with poor prognosis and TIICs in HCC. We suggest that these data may contribute to the understanding the role of C4orf47 in prognosis and immunotherapy of various cancers, including HCC, and the potential control and detailed mechanisms of C4orf47 in HCC deserve further exploration.

ACKNOWLEDGEMENT

This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (RS-2022-00165637, NRF-2021R1C1C1003333).

CONFLICT OF INTEREST

Authors declare no competing interests.

References
  1. Angell HK, Lee J, Kim KM, Kim K, Kim ST, Park SH, Kang WK, Sharpe A, Ogden J, Davenport A, Hodgson DR, Barrett JC, Kilgour E. PD-L1 and immune infiltrates are differentially expressed in distinct subgroups of gastric cancer. Oncoimmunology. 2018. 8: e1544442.
    Pubmed KoreaMed CrossRef
  2. Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015. 6: 8971.
    Pubmed KoreaMed CrossRef
  3. Balch CM, Riley LB, Bae YJ, Salmeron MA, Platsoucas CD, von Eschenbach A, Itoh K. Patterns of human tumor-infiltrating lymphocytes in 120 human cancers. Arch Surg. 1990. 125: 200-205.
    Pubmed CrossRef
  4. Bense RD, Sotiriou C, Piccart-Gebhart MJ, Haanen JBAG, van Vugt MATM, de Vries EGE, Schr철der CP, Fehrmann RSN. Relevance of Tumor-Infiltrating Immune Cell Composition and Functionality for Disease Outcome in Breast Cancer. J Natl Cancer Inst. 2016. 109: 192.
    Pubmed KoreaMed CrossRef
  5. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, Vonderheide RH, Pittet MJ, Jain RK, Zou W, Howcroft TK, Woodhouse EC, Weinberg RA, Krummel MF. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018. 24: 541-550.
    Pubmed KoreaMed CrossRef
  6. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018. 68: 394-424.
    Pubmed CrossRef
  7. Bremnes RM, Al-Shibli K, Donnem T, Sirera R, Al-Saad S, Andersen S, Stenvold H, Camps C, Busund LT. The role of tumor-infiltrating immune cells and chronic inflammation at the tumor site on cancer development, progression, and prognosis: emphasis on non-small cell lung cancer. J Thorac Oncol. 2011. 6: 824-833.
    Pubmed CrossRef
  8. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup Larsen M, Mitra S, Johansson I, Phung B, Harbst K, Vallon-Christersson J, van Schoiack A, L철vgren K, Warren S, Jirstr철m K, Olsson H, Pietras K, Ingvar C, Isaksson K, Schadendorf D, Schmidt H, Bastholt L, Carneiro A, Wargo JA, Svane IM, J철nsson G. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020. 577: 561-565.
    Pubmed CrossRef
  9. Cariani E, Missale G. Immune landscape of hepatocellular carcinoma microenvironment: Implications for prognosis and therapeutic applications. Liver Int. 2019. 39: 1608-1621.
    Pubmed CrossRef
  10. Caruso RA, Bellocco R, Pagano M, Bertoli G, Rigoli L, Inferrera C. Rognostic value of intratumoral neutrophils in advanced gastric carcinoma in a high-risk area in northern Italy. Mod Pathol. 2002. 15: 831-837.
    Pubmed CrossRef
  11. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, Varambally S. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017. 19: 649-658.
    Pubmed KoreaMed CrossRef
  12. Chen LC, Liu MY, Hsiao YC, Choong WK, Wu HY, Hsu WL, Liao PC, Sung TY, Tsai SF, Yu JS, Chen YJ. Decoding the disease-associated proteins encoded in the human chromosome 4. J Proteome Res. 2013. 12: 33-44.
    Pubmed CrossRef
  13. Choi Y, Kim JW, Nam KH, Han SH, Kim JW, Ahn SH, Park DJ, Lee KW, Lee HS, Kim HH. Systemic inflammation is associated with the density of immune cells in the tumor microenvironment of gastric cancer. Gastric Cancer. 2017. 20: 602-611.
    Pubmed CrossRef
  14. Dapito DH, Mencin A, Gwak GY, Pradere JP, Jang MK, Mederacke I, Caviglia JM, Khiabanian H, Adeyemi A, Bataller R, Lefkowitch JH, Bower M, Friedman R, Sartor RB, Rabadan R, Schwabe RF. Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4. Cancer Cell. 2012. 21: 504-516.
    Pubmed KoreaMed CrossRef
  15. Feng RM, Zong YN, Cao SM, Xu RH. Current cancer situation in China: good or bad news from the 2018 Global Cancer Statistics? Cancer Commun (Lond). 2019. 39: 22.
    Pubmed KoreaMed CrossRef
  16. Fernandez-Cruz L, Prieto M, Targarona EM, Colomer J, Casas A, Saenz A, Pl F, Morin PA. [Pancreas transplantation in 1987]. Ann Gastroenterol Hepatol (Paris). 1988. 24: 23-26.
    Pubmed
  17. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, Nair VS, Xu Y, Khuong A, Hoang CD, Diehn M, West RB, Plevritis SK, Alizadeh AA. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015. 21: 938-945.
    Pubmed KoreaMed CrossRef
  18. Gomes MA, Priolli DG, Tralh찾o JG, Botelho MF. Hepatocellular carcinoma: epidemiology, biology, diagnosis, and therapies. Rev Assoc Med Bras (1992). 2013. 59: 514-524.
    Pubmed CrossRef
  19. Goswami KK, Ghosh T, Ghosh S, Sarkar M, Bose A, Baral R. Tumor promoting role of anti-tumor macrophages in tumor microenvironment. Cell Immunol. 2017. 316: 1-10.
    Pubmed CrossRef
  20. Gu Y, Li X, Bi Y, Zheng Y, Wang J, Lim X, Huang Z, Chen L, Huang Y, Huang Y. CCL14 is a prognostic biomarker and correlates with immune infiltrates in hepatocellular carcinoma. Aging (Albany NY). 2020. 12: 784-807.
    Pubmed KoreaMed CrossRef
  21. Gy철rffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010. 123: 725-731.
    Pubmed CrossRef
  22. Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J, Flagg K, Hou J, Zhang H, Yi S, Jafari M, Lin D, Chung C, Caughey BA, Li G, Dhar D, Shi W, Zheng L, Hou R, Zhu J, Zhao L, Fu X, Zhang E, Zhang C, Zhu JK, Karin M, Xu RH, Zhang K. DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A. 2017. 114: 7414-7419.
    Pubmed KoreaMed CrossRef
  23. Harding JJ, Khalil DN, Abou-Alfa GK. Biomarkers: What Role Do They Play (If Any) for Diagnosis, Prognosis and Tumor Response Prediction for Hepatocellular Carcinoma? Dig Dis Sci. 2019. 64: 918-927.
    Pubmed CrossRef
  24. Kulik L, El-Serag HB. Epidemiology and Management of Hepatocellular Carcinoma. Gastroenterology. 2019. 156: 477-491.
    Pubmed KoreaMed CrossRef
  25. L찼nczky A, Nagy 횁, Bottai G, Munk찼csy G, Szab처 A, Santarpia L, Gy흷rffy B. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016. 160: 439-446.
    Pubmed CrossRef
  26. Laz훱r DC, Avram MF, Romo셙n I, Cornianu M, T훱ban S, Goldi A. Prognostic significance of tumor immune microenvironment and immunotherapy: Novel insights and future perspectives in gastric cancer. World J Gastroenterol. 2018. 24: 3583-3616.
    Pubmed KoreaMed CrossRef
  27. Li B, Severson E, Pignon JC, Zhao H, Li T, Novak J, Jiang P, Shen H, Aster JC, Rodig S, Signoretti S, Liu JS, Liu XS. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 2016. 17: 174.
    Pubmed KoreaMed CrossRef
  28. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017. 77: e108-e110.
    Pubmed KoreaMed CrossRef
  29. Liu LZ, Zhang Z, Zheng BH, Shi Y, Duan M, Ma LJ, Wang ZC, Dong LQ, Dong PP, Shi JY, Zhang S, Ding ZB, Ke AW, Cao Y, Zhang XM, Xi R, Zhou J, Fan J, Wang XY, Gao Q. CCL15 Recruits Suppressive Monocytes to Facilitate Immune Escape and Disease Progression in Hepatocellular Carcinoma. Hepatology. 2019. 69: 143-159.
    Pubmed CrossRef
  30. Lu C, Rong D, Zhang B, Zheng W, Wang X, Chen Z, Tang W. Current perspectives on the immunosuppressive tumor microenvironment in hepatocellular carcinoma: challenges and opportunities. Mol Cancer. 2019. 18: 130.
    Pubmed KoreaMed CrossRef
  31. Munhoz RR, Postow MA. Recent advances in understanding antitumor immunity. F1000Res. 2016. 5: 2545.
    Pubmed KoreaMed CrossRef
  32. Nguyen K, Jack K, Sun W. Hepatocellular Carcinoma: Past and Future of Molecular Target Therapy. Diseases. 2015. 4: 1.
    Pubmed KoreaMed CrossRef
  33. Ostrand-Rosenberg S. Immune surveillance: a balance between protumor and antitumor immunity. Curr Opin Genet Dev. 2008. 18: 11-18.
    Pubmed KoreaMed CrossRef
  34. Prieto J, Melero I, Sangro B. Immunological landscape and immunotherapy of hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2015. 12: 681-700.
    Pubmed CrossRef
  35. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science. 2011. 331: 1565-1570.
    Pubmed CrossRef
  36. Singal AG, El-Serag HB. Hepatocellular Carcinoma From Epidemiology to Prevention: Translating Knowledge into Practice. Clin Gastroenterol Hepatol. 2015. 13: 2140-2151.
    Pubmed KoreaMed CrossRef
  37. Soo RA, Chen Z, Yan Teng RS, Tan HL, Iacopetta B, Tai BC, Soong R. Prognostic significance of immune cells in non-small cell lung cancer: meta-analysis. Oncotarget. 2018. 9: 24801-24820.
    Pubmed KoreaMed CrossRef
  38. Sun H, Huang Q, Huang M, Wen H, Lin R, Zheng M, Qu K, Li K, Wei H, Xiao W, Sun R, Tian Z, Sun C. Human CD96 Correlates to Natural Killer Cell Exhaustion and Predicts the Prognosis of Human Hepatocellular Carcinoma. Hepatology. 2019. 70: 168-183.
    Pubmed CrossRef
  39. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019. 47: W556-W560.
    Pubmed KoreaMed CrossRef
  40. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017. 45: W98-W102.
    Pubmed KoreaMed CrossRef
  41. Tekpli X, Lien T, R첩ssevold AH, Nebdal D, Borgen E, Ohnstad HO, Kyte JA, Vallon-Christersson J, Fongaard M, Due EU, Svartdal LG, Sveli MAT, Garred 횠, OSBREAC Frigessi A, Sahlberg KK, S첩rlie T, Russnes HG, Naume B, Kristensen VN. An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment. Nat Commun. 2019. 10: 5499.
    Pubmed KoreaMed CrossRef
  42. Uehara T, Ainslie GR, Kutanzi K, Pogribny IP, Muskhelishvili L, Izawa T, Yamate J, Kosyk O, Shymonyak S, Bradford BU, Boorman GA, Bataller R, Rusyn I. Molecular mechanisms of fibrosis-associated promotion of liver carcinogenesis. Toxicol Sci. 2013. 132: 53-63.
    Pubmed KoreaMed CrossRef
  43. Vasaikar SV, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018. 46: D956-D963.
    Pubmed KoreaMed CrossRef
  44. Wallace MC, Preen D, Jeffrey GP, Adams LA. The evolving epidemiology of hepatocellular carcinoma: a global perspective. xpert Rev Gastroenterol Hepatol. 2015. 9: 765-779.
    Pubmed CrossRef
  45. Wang Z, Zhu J, Liu Y, Liu C, Wang W, Chen F, Ma L. Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma. J Transl Med. 2020. 18: 67.
    Pubmed KoreaMed CrossRef
  46. Yu SJ. A concise review of updated guidelines regarding the management of hepatocellular carcinoma around the world: 2010-2016. Clin Mol Hepatol. 2016. 22: 7-17.
    Pubmed KoreaMed CrossRef
  47. Zhang SC, Hu ZQ, Long JH, Zhu GM, Wang Y, Jia Y, Zhou J, Ouyang Y, Zeng Z. Clinical Implications of Tumor-Infiltrating Immune Cells in Breast Cancer. J Cancer. 2019. 10: 6175-6184.
    Pubmed KoreaMed CrossRef