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TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma

  • Authors:
    • Teng Long
    • Weijie Wu
    • Xin Wang
    • Minshan Chen
  • View Affiliations

  • Published online on: February 8, 2024     https://doi.org/10.3892/mco.2024.2725
  • Article Number: 27
  • Copyright: © Long et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Liver cancer is the fourth leading cause of cancer‑related mortality worldwide and hepatocellular carcinoma (HCC) is the most common primary liver cancer. In the present study, it was demonstrated that translocated promoter region (TPR) was upregulated in tumor tissues and associated with prognosis and immune infiltration in HCC. The clinical outcome of patients with HCC with aberrant expression of TPR was examined using multiple databases, including Gene Expression Omnibus, The Cancer Genome Atlas (TCGA), Genotype‑Tissue Expression, Kaplan‑Meier (KM) Plotter and Xiantao tool. The clinicopathologic characteristics of patients from TCGA database that were associated with overall survival were assessed using Cox regression and KM analysis. The potential hallmarks associated with TPR expression were further predicted by Metascape and Gene Set Enrichment Analysis, and the relationship between TPR and immune infiltration was explored using the Tumor‑Immune System Interactions Database and the Tumor Immune Estimation Resource. The results demonstrated that TPR expression was higher in HCC and its overexpression was associated with a worse prognosis, alongside a correlation with several clinical features. Furthermore, cell differentiation, a prospective new hallmark of cancer, was differentially enriched in the high TPR expression phenotype pathway. Moreover, TPR may also modulate the tumor immune microenvironment as it was significantly associated with immunoregulators and chemokines, as well as different tumor infiltration immune cells. According to the in vitro experiments, TPR silencing inhibited the phosphorylation of AKT and the proliferation of HCC cells. In summary, TPR may be a new marker and target for HCC therapy.

Introduction

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related mortality worldwide (1). Hepatocellular carcinoma (HCC) is the most common primary liver cancer and accounts for 75-85% of cases (2). Currently, the potentially curative treatments for HCC are ablation, surgical resection and transplantation (3). Despite the advances in chemotherapy, radiotherapy, immunotherapy and liver transplantation, HCC prognosis remains poor due to the high risk of recurrence and metastases (4). The highly heterogeneous nature of HCC leads to difficulties in diagnosing or predicting disease using existing biomarkers (5). Therefore, it is urgent to identify novel driver genes for HCC treatment, which will improve the survival of patients with HCC.

Translocated promoter region (TPR) is a coiled-coil homodimer and has a rod-like shape (6-8). TPR consists of two domains: An N-terminal domain consisting of ~1,600 amino acids that forms a parallel two-stranded coil and is interrupted periodically throughout its length, and a C-terminal domain that features 800 non-helical amino acids enriched in acidic residues (8-11). As with numerous transcription-regulating nucleoporins (NUPs), TPR is localized to the nucleus (12). In addition, TPR binds chromatin in vitro and is essential for the formation of heterochromatin exclusion zones near nuclear pore complexes (13,14). TPR plays a number of roles in the nucleus, including regulating the three prime repair exonuclease 2-dependent mRNA export pathway (15,16) and scaffolding ERK2(17) and MYC (18) enzymes. Several studies have demonstrated that TPR is implicated in multiple types of cancers, including lung adenocarcinoma (19,20), ependymoma (21), glioma (22) and colon cancer (23). However, little research has been conducted on HCC. Therefore, the purpose of the present study was to discover the potential mechanisms by which TPR may contribute to tumorigenesis and immune involvement in HCC.

The development of numerous database platforms has led to significant advances in cancer bioinformatics research, which allows researchers to screen markers for cancer more easily. Thus, in the present study, TPR was identified as a significantly upregulated gene in HCC using the Gene Expression Omnibus (GEO). To clarify the biological functions of TPR in HCC, Xiantao tool, The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) and the Kaplan-Meier (KM) Plotter were used to determine whether the expression of TPR was related to the clinical outcome of HCC. Next, gene-set enrichment of TPR was conducted using the Metascape database and Xiantao tool. In addition, investigation of TPR and immune cell infiltration was performed using Xiantao tool and the Tumor Immune Estimation Resource (TIMER) database. Finally, analyses of immune-modulators and chemokines were further conducted using data from the Tumor-Immune System Interactions Database (TISIDB). In the present study, TPR was identified as being important in HCC, and it was indicated that TPR may be involved in promoting tumor progression in cells.

Materials and methods

Patient datasets

The gene expression profile data (GSE36376, GSE39791 and GSE60502) were downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/geo2r) (24-26). The inclusion criteria for genomics data were as follow: i) The samples were obtained from Homo sapiens; ii) all tissues were classified as HCC or normal tissues; and iii) the sample sizes were >10 per study. GSE36376 included 240 HCC tumor tissues and 193 adjacent non-tumor tissues; GSE39791 included 72 tumor tissues and 72 matched adjacent non-tumor tissues; GSE60502 included 18 tumor tissues and 18 adjacent non-tumor tissues. GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was used to analyze differentially expressed genes (DEGs) between HCC and non-tumor samples. The cut-off was set as |log2 fold change (FC)|>1 and adjusted P<0.01. TPR expression and clinical data were obtained from TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://gtexportal.org/home/). Given that the data were obtained from the online databases, additional approval from an ethics committee was not required.

Analysis of TPR expression in tumor and normal tissues

Xiantao tool (https://www.xiantao.love/) is a platform using R software (3.6.3) for acquiring data from TCGA and GTEx. TPR expression in HCC tissues, tissues adjacent to carcinoma and normal tissues was compared using Xiantao tool and presented in box, scatter and violin plots. Diagnostic performance of TPR was assessed using receiver-operating characteristic curves performed on Xiantao tool. All the DEGs (|log2FC|>1.5 and adjusted P<0.05) gained from single gene differential analysis through Xiantao tool were presented in volcano plots.

Human tissue specimens

A total of 14 pairs of HCC and matched normal fresh frozen tissues were obtained through hepatectomy at Sun Yat-sen University Cancer Center (Guangzhou, China), from 2019 to 2021 (12 males and 2 females, ages from 20 to 78 years with an average age of 56). The patients were diagnosed according to their clinicopathologic characteristics at the Sun Yat-sen University Cancer Center. No patients had received radiotherapy and/or chemotherapy prior to surgery. Informed consent was obtained from all patients and approved (approval no. B2022-238-02) by the Research Medical Ethics Committee of Sun Yat-sen University Cancer Center.

Cell culture

The liver cancer cell lines (Hep3B, SNU449, MHCC97H, Huh7, HepG2 and HCCLM3) were obtained from the American Type Culture Collection (ATCC) and cultured according to the instructions from the ATCC. All cells were grown in DMEM supplemented with 10% FBS (both from Invitrogen; Thermo Fisher Scientific, Inc.) at 37˚C and 5% CO2. All cell lines in the present study were authenticated utilizing short-tandem repeat profiling.

RNA extraction and reverse transcription-quantitative PCR (RT-qPCR)

The total RNA of the liver cancer cell lines (Hep3B, SNU449, MHCC97H, Huh7, HepG2 and HCCLM3) or tissue was isolated utilizing TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. First-strand cDNA was synthesized utilizing the Revert Aid First Strand cDNA Synthesis Kit (Fermentas; Thermo Fisher Scientific, Inc.). The thermocycling conditions used were as follows: 37˚C for 15 min, 95˚C for 5 sec and were then cooled to 4˚C upon completion. Quantitative PCR assays were performed using a A28134 QuantStudio® 5 Real-Time PCR Instrument (Thermo Fisher Scientific, Inc.) and iTaq Universal SYBR Green Supermix reagent (Bio-Rad Laboratories, Inc.). The thermocycling conditions used were as follows: 95˚C for 30 sec, 40 cycles at 95˚C for 3 sec and 60˚C for 30 sec, then 60˚C for 20 sec and 95˚C for 1 sec. The relative expression of each gene was calculated using the 2-ΔΔCq method with GAPDH as the internal reference (27). The primers used to amplify the indicated genes are shown in Table SI.

Interaction network analysis

The GeneMANIA database (http://www.genemania.org) was used to explore the genes that interacted with TPR. The Search Tool for Interaction Gene/Proteins (STITCH) website (http://stitch.embl.de/) was used to analyze the protein-protein interactions of TPR.

Tumor immune infiltration analysis

TIMER (https://cistrome.shinyapps.io/timer/) and Xiantao tool were used to analyze the infiltration levels of different immune cells. TIMER was also applied to explore the interrelation between TPR expression and different gene marker sets of immune cells. The correlations were evaluated by purity-correlated partial Spearman's correlation. An integrated repository portal for TISIDB (http://cis.hku.hk/TISIDB/) was used to investigate the relationship between TPR and immunoinhibitors, immunostimulators, chemokines and receptors.

Enrichment analysis

Metascape (http://metascape.org/) and Xiantao tool was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to explore the biological processes (BPs), cellular components (CCs) and molecular functions (MFs) of TPR in HCC. Gene Set Enrichment Analysis (GSEA) was used to probe the potential mechanisms of TPR performed on Xiantao tool. Gene sets with false discovery rate (FDR) <0.25 and P. adjust <0.05 were considered as significant.

Small interfering RNA (siRNA) treatment

The oligonucleotide sequences targeting TPR mRNA were as follows: #1, GCAGCTTGTTGATTCCATA (5'-3') and #2, GGAGCGATCTGAAACAGAA (5'-3') synthesized from Guangzhou RiboBio Co., Ltd. The siRNA negative control (siN0000001-1-5) is designed and produced by Guangzhou RiboBio Co., Ltd. and its sequence is proprietary (28). Transfection was performed according to the manufacturer's instructions using Lipofectamine RNAi MAX transfection reagent (Invitrogen; Thermo Fisher Scientific, Inc.) and 50 nM siRNA. siRNA transfection was performed at 37˚C for 6 h and the culture medium was then replaced with fresh culture medium. Follow-up procedures were performed 24 h later.

Western blotting (WB)

Briefly, the liver cancer cell lines Hep3B and SNU449 were collected, lysed in RIPA buffer (150 mM NaCl, 0.5% EDTA, 50 mM Tris, 0.5% NP40, pH=8.0) and centrifuged for 20 min at 13,500 x g and 4˚C. The protein concentration was measured using a BCA Protein Assay Kit (Beijing Solarbio Science & Technology Co., Ltd.). The proteins (20 µg/lane) were separated using SDS-PAGE electrophoresis in 4 to 20% polyacrylamide gels and transferred to PVDF membranes (Invitrogen; Thermo Fisher Scientific, Inc.). The PVDF containing protein membranes were blocked in 5% skim milk at room temperature for 120 min. The membranes were then incubated overnight with primary antibodies against AKT (1:1,000), phosphorylated (p)-AKT (1:1,000) or GAPDH (1:1,000) at 4˚C. Membranes were then washed with TBST with 0.1% Tween-20 and incubated with goat anti-rabbit IgG (H&L) HRP secondary antibodies (1:10,000) at 25˚C for 1 h. The protein bands were then visualized using the ECL chemiluminescence system (Pierce; Thermo Fisher Scientific, Inc.). The antibodies used in the present study are shown in Table SII.

3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay

An MTT assay was conducted to measure cell viability. Briefly, SNU449 or Hep3B cells were seeded at a density of 2,000 cells per well in a 96-well microplate. The cells were incubated with MTT at 37˚C for 4 h, then the culture medium was removed, and 200 µl dimethylsulfoxide (DMSO) was added to dissolve the purple formazan. The optical density (OD) was detected at 490 nm with the microplate reader once per day for 5 days. The results are presented as the mean ± SD of three independent experiments.

Statistical analysis

Data were collected from three independent experiments. The GraphPad Prism 9 (GraphPad Software; Dotmatics) and the SPSS software (version 16.0, SPSS Inc.) were used for data analysis. Data were presented as the mean ± SD. Unpaired Student's t-test was used to analyze differences between groups and one-way analysis of variance with Tukey's post hoc test was used to analyze multiple groups. Survival analysis was performed using the KM method, and differences among the survival curves were analyzed with the log-rank test. The follow-up threshold was 30 months. Wilcoxon rank sum test was used to analyze α-fetoprotein (AFP). Dunn's test was used to analyze the pathological stage. Two-way ANOVA with Dunnett's multiple comparison test was used to analyze the MTT assays. P<0.05 was considered to indicate a statistically significant difference.

Results

High expression of TPR in HCC and pan-cancer

The flow diagram presented in Fig. 1 was constructed to reveal the process of the present study. To identify the key genes in the progression of HCC, GSE36376, GSE39791 and GSE60502 datasets were analyzed to explore the DEGs in HCC. As demonstrated by Venn diagram (Fig. 2A), 187 congruous DEGs were identified. Notably, TPR was the only upregulated gene and there were no downregulated genes included in the overlap of the three datasets (Figs. 2B and S1). An additional 186 genes were upregulated or downregulated in two datasets and expressed inversely in the other dataset.

Figure 2

Differential expression map of TPR. (A) Venn diagrams of DEGs and (B) identification of upregulated DEGs in the three gene expression profile datasets. (C) The expression of TPR in tumor and normal tissues of pan-cancer in TCGA and Genotype-Tissue Expression. *P<0.05, **P<0.01, ***P<0.001 using the Wilcoxon rank sum test. (D) The expression of TPR in normal and HCC tissues in TCGA database. ***P<0.001 using the Wilcoxon rank sum test. (E) TPR expression in HCC tissues and matched normal tissues from TCGA. ***P<0.001 using the paired t-test. (F) The relative mRNA expression level of TPR in 14 paired HCC and para-cancer tissues were determined by RT-qPCR. ****P<0.0001 using the paired t-test. (G) The transcriptional level of TPR was determined in the indicated cell lines by RT-qPCR. The relative mRNA levels of TPR were normalized to the GAPDH level in the indicated cells. Results are presented as the mean ± SD of three independent experiments. Correlation analysis between TPR and (H) PDCD1, (I) VEGFA, (J) FGFR4, (K) PDGFB and (L) KIT at the mRNA level using Spearman's correlation. TPR, translocated promoter region; DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; HCC, hepatocellular carcinoma; ns, no significance; RT-qPCR, reverse transcription-quantitative PCR; PDCD1, programmed cell death 1; VEGFA, vascular endothelial growth factor A; FGFR4, fibroblast growth factor receptor 4; PDGFB, platelet derived growth factor subunit B; KIT, KIT proto-oncogene.

The differential expression of TPR in HCC tissues and normal tissues was then explored. Among the expression of TPR in 33 types of tumors assessed from TCGA and GTEx, 24 types expressed significantly more TPR than corresponding normal tissues, 6 types demonstrated no significant difference and 3 types lacked sufficient samples (Fig. 2C). It was further identified that the expression of TPR was significantly higher in 374 tumor cases compared with 50 normal cases (P<0.001; Fig. 2D) and 50 tumor tissues compared with their matched non-tumor tissues from TCGA (P<0.001; Fig. 2E).

Furthermore, RT-qPCR was used to determine TPR expression in 14 pairs of HCC and peritumoral tissues. The findings demonstrated a higher TPR expression in most tumor tissues (Fig. 2F). Subsequently, TPR expression was confirmed at the mRNA level in liver cancer cell lines (Fig. 2G). Next, the correlation of TPR expression with several types of current HCC therapeutic targets, including programmed cell death protein 1 (PDCD1) (29), vascular endothelial growth factor A (VEGFA) (30), fibroblast growth factor receptor 4 (FGFR4) (31), platelet derived growth factor subunit B (PDGFB) (32) and KIT proto-oncogene (KIT) (33), were investigated (Fig. 2H and L). Notably, there were markedly positive correlations between the expression of TPR and these targets. Collectively, these results demonstrated that TPR was highly expressed in HCC and was associated with several existing therapeutic targets.

Association between TPR and clinical features

It was then determined whether the expression of TPR is associated with the clinicopathologic variables of patients with HCC. A total of 374 patients with HCC from TCGA, including 353 males and 121 females, were divided into two categories according to the median expression of TPR: The low expression group and the high expression group, and the clinical and gene expression data of these patients were subsequently analyzed (Table SIII). The results revealed that TPR expression was significantly associated with histologic grade (P=0.046), age (P=0.042), height (P=0.039) and weight (P=0.034). An association between TPR expression and other clinicopathologic features was not identified. Based on univariate analyses using logistic regression, it was determined that TPR upregulation in HCC was significantly associated with age (P=0.020), histologic grade (P=0.016) and AFP level (P=0.007) (Table I). As revealed in Fig. 3, TPR expression was significantly associated with AFP (P<0.001) and pathological stage (stage I vs. stage III, P=0.019), but not sex, tumor status, tumor (T) stage, adjacent hepatic tissue inflammation, vascular invasion, residual tumor or ethnicity. These results indicated that the expression of TPR was associated with several clinicopathologic variables of patients with HCC.

Table I

Expression of TPR associated with clinicopathological characteristics (logistic regression).

Table I

Expression of TPR associated with clinicopathological characteristics (logistic regression).

CharacteristicsTotal (N)Odds ratio (OR)P-value
T stage (T1 and T2 vs. T3 and T4)3710.836 (0.521-1.337)0.454
N stage (N0 vs. N1)2580.366 (0.018-2.905)0.387
M stage (M0 vs. M1)2723.137 (0.396-63.865)0.325
Pathologic stage (Stage I and Stage II vs. Stage III and Stage IV)3500.787 (0.485-1.271)0.328
Tumor status (Tumor free vs. With tumor)3550.754 (0.494-1.148)0.188
Sex (Male vs. Female)3740.764 (0.494-1.179)0.225
Race (Asian vs. Black or African American and White)3621.127 (0.744-1.709)0.571
Age (≤60 vs. >60)3731.627 (1.082-2.456)0.020
Weight (≤70 vs. >70)3461.461 (0.956-2.237)0.080
Height (<170 vs. ≥170)3411.189 (0.772-1.834)0.433
BMI (≤25 vs. >25)3371.146 (0.747-1.760)0.532
Residual tumor (R0 vs. R1 and R2)3450.473 (0.161-1.249)0.144
Histologic grade (G1 and G2 vs. G3 and G4)3690.592 (0.385-0.906)0.016
Adjacent hepatic tissue inflammation (None vs. Mild and Severe)2370.774 (0.463-1.290)0.327
AFP (ng/ml) (≤400 vs. >400)2800.455 (0.254-0.799)0.007
Albumin (g/dl) (<3.5 vs. ≥3.5)3000.817 (0.473-1.401)0.465
Prothrombin time (≤4 vs. >4)2971.077 (0.655-1.774)0.770
Child-Pugh grade (A vs. B and C)2410.550 (0.219-1.329)0.189
Fibrosis ishak score (0 and 1/2 vs. 3/4 and 5/6)2150.668 (0.388-1.146)0.144
Vascular invasion (No vs. Yes)3180.755 (0.474-1.200)0.235

[i] TPR, translocated promoter region.

Clinical value of TPR in patient prognosis

To evaluate the clinical significance of TPR expression, survival rates between high and low TPR expression levels were compared to determine the association between TPR expression and prognosis. The KM survival analysis demonstrated that there were significant differences in overall survival (OS) relapse-free survival (RFS) and progression-free survival (PFS), between the TPR high and low expression groups (P=0.08; P=0.033; P=0.02, respectively) (Fig. 4A-C). The univariate Cox proportional hazards model was then applied, and T stage, metastasis stage, pathological stage and tumor status were identified as potential prognostic factors with P<0.05 for OS. In addition, after multivariate analyses was applied, the expression of TPR was not identified as an independent risk prognostic factor for OS among patients with HCC (Table II). Based on the risk factors identified in the multivariate analysis, nomograms were developed to predict 1-, 3-5-year OS rates for HCC patients (Fig. 4D). Calibration plots were constructed to evaluate the agreement between the predicted and the actual OS using the prognosis model (Fig. 4E). In addition, the area under curve (AUC) of TPR in HCC was 0.885 (95% CI, 0.852-0.918), which suggested high diagnostic accuracy of TPR in HCC (Fig. 4F). These results indicated that TPR was a valuable marker to predict clinical outcomes, along with other clinical features in patients with HCC.

Table II

Univariate and multivariate analyses of overall survival of patients with hepatocellular carcinoma from The Cancer Genome Atlas (Cox regression).

Table II

Univariate and multivariate analyses of overall survival of patients with hepatocellular carcinoma from The Cancer Genome Atlas (Cox regression).

 Univariate analysisMultivariate analysis
CharacteristicsTotal (N)Hazard ratio (95% CI)P-valueHazard ratio (95% CI)P-value
T stage370    
     T1 and T2277Reference   
     T3 and T4932.598 (1.826-3.697) <0.0011.769 (0.238-13.138)0.577
M stage272    
     M0268Reference   
     M144.077 (1.281-12.973)0.0171.658 (0.384-7.159)0.498
Pathologic stage349    
     Stage I and Stage II259Reference   
     Stage III and Stage IV902.504 (1.727-3.631) <0.0011.423 (0.193-10.480)0.729
Tumor status354    
     Tumor-free202Reference   
     With tumor1522.317 (1.590-3.376) <0.0011.889 (1.180-3.022)0.008
Child-Pugh grade240    
     A218Reference   
     B and C221.643 (0.811-3.330)0.168  
Vascular invasion317    
     No208Reference   
     Yes1091.344 (0.887-2.035)0.163  
Race361    
     Asian159Reference   
     Black or African2021.341 (0.926-1.942)0.121  
     American & White     
TPR373    
     Low186Reference   
     High1871.367 (0.964-1.938)0.0791.575 (0.990-2.507)0.055

[i] Bold indicates P<0.05. TPR, translocated promoter region.

DEGs, enrichment and interaction networks analysis

After dividing the patients with HCC into two groups according to their TPR expression, an analysis of the DEGs from the two groups was performed. Based on the analysis, 676 DEGs were identified, among them, 498 genes were upregulated and 169 genes were downregulated (Fig. 5A). The top 10 most downregulated genes were AC107396.1, SAA2, SAA1, HAMP, SAA2-SAA4, CLEC1B, CLEC4G, ACTBP12, AC091729.2 and OR52E8 (data not shown). In addition, the top 10 most upregulated genes (LGALS14, HMGA2, SLC6A14, SLC6A15, MYO3A, FER1L6, BPIFA1, CAPN6, MIR483 and COL2A1) are shown in the gene expression heatmap in Fig. 5B. Notably, HMGA2, which had been demonstrated to be associated with tumor progression (34-36), was the upregulated gene of significant expression according to the volcano plots (Fig. 5A).

Therefore, the potential biological functions of TPR in HCC were examined. GO and KEGG analyses were performed using the Metascape database, and the top 20 GO enriched pathways are listed in Fig. 5C. It was notable that the GO enriched items included cell differentiation, which is involved in tumor progression. Moreover, a total of 132 BPs, 34 CCs, 37 MFs and 7 KEGG annotations were associated with TPR co-expressed genes, the results of which are shown by bubbly plots (Fig. 5D-G). Next, GSEA between high and low TPR expression data sets were conducted to explore the signaling pathways differentially activated in HCC. Gene sets with false discovery rate (FDR) <0.25 and P.adjust <0.05 were considered as significant. The top five most significant signaling pathways enriched in the high TPR expression phenotype according to their normalized enrichment score (NES) are listed in Fig. 5H, including retinoblastoma gene in cancer (NES=2.801; FDR=0.002; P.adjust=0.003), resolution of sister chromatid cohesion (NES=2.703; FDR=0.002; P.adjust=0.003), mesodermal commitment pathway (NES=2.681; FDR=0.002; P.adjust=0.003), mitotic prometaphase (NES=2.658; FDR=0.002; P.adjust=0.003) and endoderm differentiation (NES=2.650; FDR=0.002; P.adjust=0.003).

Finally, the gene-gene interaction networks of TPR constructed using GeneMania indicated the top 20 most frequently altered genes (Fig. 5I), while the protein-protein interaction networks generated using STITCH presented the top 10 proteins that most interacted with TPR (Fig. 5J; Table III). These results prompted a further investigation into the potential biological functions of TPR in HCC.

Table III

Annotation and respective co-expression scores of proteins that interact with TPR.

Table III

Annotation and respective co-expression scores of proteins that interact with TPR.

Gene symbolAnnotationCombined score
NUP93Nucleoporin 93 kDa0.995
NUP153Nucleoporin 153 kDa0.994
NUP98Nucleoporin 98 kDa0.994
NXF1Nuclear RNA export factor 10.991
NUP107Nucleoporin 107 kDa0.991
U2AF2U2 small nuclear RNA auxiliary factor 20.987
RANBP2RAN binding protein 20.979
NUP205Nucleoporin 205 kDa0.978
NUP133Nucleoporin 133 kDa0.972

[i] TPR, translocated promoter region.

Correlation between TPR expression and tumor immune infiltration

GSEA between high and low TPR expression data sets also identified several immune and inflammation-related pathways enriched in the high TPR expression phenotype, including resistin as a regulator of inflammation, immunoregulatory interactions between a lymphoid and a non-lymphoid cell, the inflammatory response pathway, ADORA2B-mediated anti-inflammatory cytokine production, and intestinal immune network for IgA production (data not shown). Given that targeting the tumor microenvironment (TME) is a new treatment strategy for HCC and that immune infiltration is a core component of the TME, the correlation between TPR and tumor immune infiltration was determined to further investigate the effect of TPR on the TME. For this purpose, the associations between TPR expression and the number of different tumors infiltrating immune cells in HCC were analyzed utilizing the Xiantao tool. The results indicated that the expression of TPR was negatively correlated with StromalScore (ρ=-0.121; P=0.020), ESTIMATEScore (ρ=-0.192; P<0.001) and ImmuneScore (ρ=-0.218; P<0.001) (Fig. 6A). The correlations between TPR and infiltrating immune cells are shown in Fig. 6B. Elevated expression of TPR was positively correlated with the infiltration of T helper cells (ρ=0.436), Th2 cells (ρ=0.319) and central memory T cell (Tcm) (ρ=0.276), and negatively correlated with cytotoxic cells (ρ=0.421), dendritic cells (DCs; ρ=-0.382) and plasmacytoid dendritic cells (pDCs; ρ=-0.366) (all P<0.001) (Fig. 6C-H).

A validation study of TPR expression and the diverse immune signature was further conducted to understand the crosstalk between TPR and immune response by TIMER. The gene markers were used to characterize immune cells (Table IV) and functional T cells (Table V). The results revealed that the expression of TPR was significantly associated with most immune and T cell markers after adjusting for tumor purity. As shown in the aforementioned tables, markers of M1 macrophages (IRF5, PTGS2 and NOS2), M2 macrophages (CD163, VSIG4 and MS4A4A), Treg (FOXP3, CCR8 and TGFB1) and exhausted T cells (HAVCR2, CXCL13 and LAYN) were correlated with TPR in HCC (P<0.001). It was suggested TPR could regulate exhaustion and macrophage polarization in HCC. Next, the association between TPR with immunoinhibitors, such as CSF1R (ρ=-0.285; P<0.001), HAVCR2 (ρ=-0.275; P<0.001) and TGFBR1 (ρ=0.133; P=0.0104) (Fig. 7A), and immunostimulators, including CD40 (ρ=-0.296; P<0.001), IL6R (ρ=0.395; P<0.001) and TNFRSF14 (ρ=-0.388; P<0.001) (Fig. 7B), were investigated. In addition, the association between the expression of TPR and chemokines, containing CCL5 (ρ=0.377; P<0.001), CCL14 (ρ=-0.382; P<0.001) and CXCL2 (ρ=-0.324; P<0.001) (Fig. 7C), were explored. Consistently, TPR expression was identified to be significantly associated with chemokine receptors, such as CCR5 (ρ=-0.181; P<0.001), CXCR3 (ρ=-0.22; P<0.001) and CXCR6 (ρ=-0.238; P<0.001) (Fig. 7D). These results demonstrated that TPR was an immunoregulatory factor in HCC and played an integral role in regulating the immune response.

Table IV

Correlation analysis between TPR and related gene markers of immune cells in TIMER.

Table IV

Correlation analysis between TPR and related gene markers of immune cells in TIMER.

 HCC
 NonePurity
DescriptionGene markersCorP-valueCorP-value
B cellsCD190.1460.0050.211<0.001
 CD79A0.0770.1380.193<0.001
T cellsCD3D0.0200.7030.1210.025
 CD3E0.0780.1330.212<0.001
 CD20.0560.2830.1830.001
CD8+ T cellsCD8A0.1020.0490.207<0.001
 CD8B-0.0140.7860.0800.141
MonocytesCD860.197<0.0010.345<0.001
 CSF1R0.1340.0100.272<0.001
TAMCCL20.1110.0330.233<0.001
 CD680.1680.0010.264<0.001
 IL100.1760.0010.287<0.001
M1IRF50.449<0.0010.462<0.001
 PTGS20.267<0.0010.420<0.001
 NOS20.222<0.0010.236<0.001
M2CD1630.1410.0070.256<0.001
 VSIG40.0910.0790.211<0.001
 MS4A4A0.0960.0640.219<0.001
NeutrophilsCEACAM80.0250.6320.0480.371
 ITGAM0.223<0.0010.318<0.001
 CCR70.1710.0010.307<0.001
NK cellsKIR2DL1-0.0510.331-0.0510.349
 KIR2DL30.1110.0320.1560.004
 KIR2DL40.0350.4960.0770.156
 KIR3DL10.0530.3070.1050.052
 KIR3DL20.0740.1520.1400.009
 KIR3DL30.0160.7620.0010.991
 KIR2DS40.0430.4060.0520.334
DC cellsHLA-DPB10.0890.0870.199<0.001
 HLA-DQB10.0150.7720.1160.031
 HLA-DRA0.1530.0030.272<0.001
 HLA-DPA10.1550.0030.276<0.001
 CD1C0.233<0.0010.333<0.001
 NRP10.534<0.0010.574<0.001
 ITGAX0.276<0.0010.403<0.001

[i] Analyzed using Spearman's correlation. None, correlation without adjustment. Purity, correlation adjusted by purity; Cor, ρ value of Spearman's correlation; TPR, translocated promoter region; TAM, tumor-associated macrophage.

Table V

Correlation analysis between TPR and related gene markers of several types of T cells in TIMER.

Table V

Correlation analysis between TPR and related gene markers of several types of T cells in TIMER.

 HCC
 NonePurity
DescriptionGene markersCorP-valueCorP-value
Th1TBX210.0600.2480.1560.004
 STAT40.1380.0080.209<0.001
 STAT10.485<0.0010.549<0.001
 TNF0.240<0.0010.354<0.001
 IFNG0.0780.0330.154<0.001
Th1-likeHAVCR20.172<0.0010.321<0.001
 CXCR30.0890.0890.1850.001
 BHLHE400.363<0.0010.394<0.001
 CD40.1570.0020.242<0.001
Th2STAT60.449<0.0010.441<0.001
 STAT5A0.318<0.0010.385<0.001
TregsFOXP30.230<0.0010.273<0.001
 CCR80.423<0.0010.531<0.001
 TGFB10.282<0.0010.398<0.001
Resting TregsFOXP30.230<0.0010.273<0.001
 IL2RA0.225<0.0010.347<0.001
Effector Treg T cellsFOXP30.230<0.0010.273<0.001
 CCR80.423<0.0010.531<0.001
 TNFRSF90.359<0.0010.453<0.001
Effector T cellsCX3CR10.409<0.0010.457<0.001
 FGFBP2-0.0190.709-0.0090.864
 FCGR3A0.1650.0010.267<0.001
Naive T cellsCCR70.171<0.0010.307<0.001
 SELL0.258<0.0010.374<0.001
Effector memory T cellsDUSP40.287<0.0010.415<0.001
 GZMK-0.0170.7470.0790.144
 GZMA-0.0840.1060.0070.895
Resident memory T cellsCD690.202<0.0010.340<0.001
 CXCR60.0790.1310.206<0.001
 MYADM0.562<0.0010.617<0.001
General memory T cellsCCR70.171<0.0010.307<0.001
 SELL0.258<0.0010.374<0.001
 IL7R0.328<0.0010.465<0.001
Exhausted T cellsHAVCR20.172<0.0010.321<0.001
 LAG30.0630.2240.1150.032
 CXCL130.1150.0270.1810.001
 LAYN0.291<0.0010.372<0.001

[i] Analyzed using Spearman's correlation. None, correlation without adjustment; Purity, correlation adjusted by purity; Cor, ρ value of Spearman's correlation. TPR, translocated promoter region; Th, T helper cell; Treg, regulatory T cell.

TPR promotes phosphorylation of AKT and proliferation of HCC cells

To confirm the bioinformatics results and to verify the effect of TPR on HCC cells, TPR-silenced SNU449 and Hep3B cell lines were constructed (Fig. 8A). It has been reported that regulation of TPR expression affects the phosphorylation activity of the AKT pathway (37). Consistently, the results of WB demonstrated that silencing TPR expression decreased the phosphorylation levels of AKT (Fig. 8B). Next, an MTT assay was conducted to explore the effect of siTPR on the proliferation ability of HCC cells (Fig. 8C). The results demonstrated that after silencing TPR in SNU449 and Hep3B cells, proliferation was significantly inhibited. Therefore, TPR may promote the proliferation of HCC cells through the AKT pathway.

Discussion

In the present study, the expression level of TPR in HCC and the clinical significance of the gene was comprehensively evaluated by bioinformatics methods. It was demonstrated that high expression of TPR in HCC was associated with poor prognosis. In addition, expression of TPR was closely associated with the infiltration of various immune cells, immunomodulators and chemokines. Collectively, these results offer new insights into the roles TPR plays in HCC, which may have a prognostic value for tumor immune infiltration.

Studies have demonstrated that TPR is involved in several types of cancer. For instance, Wei et al (19) suggested that the role of a novel TPR-ROS1 fusion was as an oncogenic driver in metastatic non-small cell lung cancer (NSCLC); Choi et al (20) reported that the TRP-ALK protein had the potential to transform cells and respond to ALK inhibitor treatment in NSCLC; Dewi et al (21) reported that TPR regulated heat shock transcription factor 1 mRNA trafficking, maintained MTORC1 activity to phosphorylate ULK1, and prevented macroautophagy/autophagy induction in ependymoma. According to these studies, TPR may affect cancer in a significant way and several types of malignancies may be treated by targeting it. However, it is unclear whether TPR has clinical significance in HCC or whether it regulates tumor immunity.

In the present study, according to bioinformatic analyses of high throughput RNA-sequencing data from TCGA, TPR was expressed at a significantly higher level in HCC tissues than in paired normal tissues, indicating that TPR participated in tumorigenesis and progression. Further investigation was conducted into the link between TPR expression and clinicopathological parameters and it was identified that high expression levels of TPR protein were associated with AFP and pathological stage. In addition, a prognostic gene signature model based on the KM curve of TPR demonstrated that TPR is valuable for predicting HCC survival, and there was a decrease in OS, RFS and PFS in patients with HCC with higher TPR expression. Additionally, it was suggested that the expression level of TPR could be used as a diagnostic indicator of HCC with an AUC of 0.885. Taken together, these results support the hypothesis that TPR could be a prognostic biomarker for HCC.

In addition, valuable insight into the potential key pathways of TPR in HCC were provided in the present study. Metascape revealed that cell differentiation was included in the enriched terms. Consistently, GSEA also demonstrated that differentiation was enriched in the TPR high expression phenotype. There is evidence that cancer pathogenesis is driven by evading or escaping from terminal differentiation after unlocking the normally restricted capability for phenotypic plasticity (38). Thus, cell plasticity is a promising target for anticancer therapy. The potential connection between TPR and cell differentiation may be a differentiation therapeutic target in HCC.

A significant correlation between TPR expression and immune infiltration in HCC was also demonstrated in the present study. It was determined that TPR was positively correlated to T helper cells, Th2 cells and Tcm, whereas TPR had an inverse correlation with cytotoxic cells, DC and pDC. Furthermore, TPR was significantly correlated with several immune cell marker sets. For instance, TPR expression was associated with markers of M1 macrophages, IRF5, PTGS2 and NOS2, as well as markers of M2 macrophages, CD163, VSIG4 and MS4A4A. There is an important role for macrophages in proliferation (39), angiogenesis (40), invasion and metastasis (41). According to these results, TPR may regulate polarization of tumor-associated macrophages. In addition, TPR upregulation was also closely associated with Treg markers (FOXP3, CCR8 and TGFB1) and exhausted T cells markers (HAVCR2, LAG3, CXCL13 and LAYN). The main strategy of immunotherapy is to block immune checkpoints (42). Therefore, it is essential to increase the response of tumor cells to immune checkpoint inhibitors and cytokines (43,44). Given that the upregulation of TPR was significantly correlated with immune regulators and chemokines, it is proposed that targeting TPR may improve immunotherapy effectiveness.

Furthermore, in the present study, it was demonstrated that TPR regulated the AKT signaling pathway and tumorigenicity. TPR silencing decreased the phosphorylation levels of AKT and the proliferation of HCC cells. Recently, studies have demonstrated that the AKT pathway regulates cell proliferation and survival (45,46). Consistently, the AKT pathway has been revealed to be hyperactivated in HCC (47). The aforementioned results suggest that acceleration of the malignant behaviors of HCC cells by TPR may be through activating the AKT pathway, although the exact mechanism requires further investigation.

In spite of these results, the present study has some limitations. The results of the present study may be influenced by the fact that most of the data is based on online platforms, which are continuously updated and extended. In addition, the clinical background of these patients is unclear and there may be data collection bias. To avoid confounding differences in the clinical outcomes due to the tumor burdens with those stemming from baseline differences, future research will pay more attention to background information from patients. Secondly, the specific mechanism by which TPR regulates the AKT pathway and other pathways related to TPR in HCC need to be further explored in future studies.

Supplementary Material

Venn diagram of identified downregulated differentially expressed genesin three gene expression profile datasets.
Primers used to amplify the indicated genes.
Antibodies used in western blotting
Association between the expression of TPR and the clinicopathological characteristics of patients

Acknowledgements

Not applicable.

Funding

Funding: No funding was received.

Availability of data and materials

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

Authors' contributions

TL, WW, XW and MC contributed to the conception and design of the study. TL and WW were responsible for the analysis and interpretation of the data. TL and WW drafted the manuscript. XW and MC revised the manuscript critically for intellectual content. TL, WW, XW and MC confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

The present study was conducted according to the ethical guidelines of the 1975 Declaration of Helsinki. The study was approved (approval no. B2022-238-02) by the Research Medical Ethics Committee of Sun Yat-sen University Cancer Center (Guangzhou, China). Informed consent was obtained from all patients.

Patient consent for publication

All patients signed the informed consent for second use of pathological data and biological specimens.

Competing interests

The authors declare that they have no competing interests.

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Long T, Wu W, Wang X and Chen M: TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma. Mol Clin Oncol 20: 27, 2024.
APA
Long, T., Wu, W., Wang, X., & Chen, M. (2024). TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma. Molecular and Clinical Oncology, 20, 27. https://doi.org/10.3892/mco.2024.2725
MLA
Long, T., Wu, W., Wang, X., Chen, M."TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma". Molecular and Clinical Oncology 20.4 (2024): 27.
Chicago
Long, T., Wu, W., Wang, X., Chen, M."TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma". Molecular and Clinical Oncology 20, no. 4 (2024): 27. https://doi.org/10.3892/mco.2024.2725