Protein tyrosine phosphatase non-receptor II: A possible biomarker of poor prognosis and mediator of immune evasion in hepatocellular carcinoma

Abstract

BACKGROUND

The incidence of primary liver cancer is increasing year by year. In 2022 alone, more than 900000 people were diagnosed with liver cancer worldwide, with hepatocellular carcinoma (HCC) accounting for 75%-85% of cases. HCC is the most common primary liver cancer. China has the highest incidence and mortality rate of HCC in the world, and it is one of the malignant tumors that seriously threaten the health of Chinese people. The onset of liver cancer is occult, the early cases lack typical clinical symptoms, and most of the patients are already in the middle and late stage when diagnosed. Therefore, it is very important to find new markers for the early detection and diagnosis of liver cancer, improve the therapeutic effect, and improve the prognosis of patients. Protein tyrosine phosphatase non-receptor 2 (PTPN2) has been shown to be associated with colorectal cancer, triple-negative breast cancer, non-small cell lung cancer, and prostate cancer, but its biological role and function in tumors remain to be further studied.

AIM

To combine the results of relevant data obtained from The Cancer Genome Atlas (TCGA) to provide the first in-depth analysis of the biological role of PTPN2 in HCC.

METHODS

The expression of PTPN2 in HCC was first analyzed based on the TCGA database, and the findings were then verified by immunohistochemical staining, quantitative real-time polymerase chain reaction (qRT-PCR), and immunoblotting. The value of PTPN2 in predicting the survival of patients with HCC was assessed by analyzing the relationship between PTPN2 expression in HCC tissues and clinicopathological features. Finally, the potential of PTPN2 affecting immune escape of liver cancer was evaluated by tumor immune dysfunction and exclusion and immunohistochemical staining.

RESULTS

The results of immunohistochemical staining, qRT-PCR, and immunoblotting in combination with TCGA database analysis showed that PTPN2 was highly expressed and associated with a poor prognosis in HCC patients. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that PTPN2 was associated with various pathways, including cancer-related pathways, the Notch signaling pathway, and the MAPK signaling pathway. Gene Set Enrichment Analysis showed that PTPN2 was highly expressed in various immune-related pathways, such as the epithelial mesenchymal transition process. A risk model score based on PTPN2 showed that immune escape was significantly enhanced in the high-risk group compared with the low-risk group.

CONCLUSION

This study investigated PTPN2 from multiple biological perspectives, revealing that PTPN2 can function as a biomarker of poor prognosis and mediate immune evasion in HCC.

Key Words: Protein tyrosine phosphatase non-receptor 2; Hepatocellular carcinoma; Immune evasion; Biomarker; Immunotherapy; Prognosis

Core Tip: Our results of immunohistochemical staining, quantitative real-time polymerase chain reaction, and immunoblotting in combination with those obtained from The Cancer Genome Atlas database showed that protein tyrosine phosphatase non-receptor 2 (PTPN2) was highly expressed in patients with hepatocellular carcinoma (HCC) and was associated with a poor prognosis. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that PTPN2 was associated with multiple pathways, including cancer-related pathway, the Notch signaling pathway, and the MAPK signaling pathway. Analysis by constructing a risk model based on PTPN2 revealed that immune escape was significantly enhanced in the high-risk group compared with the low-risk group. In this study, PTPN2 was investigated from multiple biological perspectives, and was found to have the potential to serve as a biomarker of poor prognosis in HCC and to mediate immune escape.

INTRODUCTION

Primary liver cancer is the seventh most common cancer worldwide and the second leading cause of cancer death[1,2]. Globally, primary liver cancer encompasses mainly hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, and other rare tumors, among which HCC is the main type of primary liver cancer, accounting for approximately 75% of liver cancer cases[3]. The prognosis of HCC is poor worldwide[4], with roughly equal morbidity and mortality rates. Due to its complex etiological variables and insidious clinical features, most cases of HCC are diagnosed at an advanced stage, and even in some patients who can be treated surgically, the postsurgical recurrence rate is high[5-7]. With the gradual increase in the understanding of HCC, combined therapeutic approaches such as surgical intervention, radiotherapy, chemotherapy, and biotherapy have yielded promising results in the treatment of HCC. Despite rapid advances in molecular targeted therapies and immunotherapies over the decades, there has been limited improvement in HCC prognosis[8,9]. Therefore, early detection and treatment are the key to successful treatment of HCC, and it is very important to further search for highly specific molecular biomarkers and therapeutic targets.

Protein tyrosine phosphatase non-receptor 2 (PTPN2) is a member of the PTP family of signaling proteins that regulate receptor tyrosine kinase signaling and play an important role in cells[10]. PTPN2, also called T cell protein tyrosine phosphatase, is a signal molecule that regulates tyrosine kinase phosphorylation and dephosphorylation involved in cell signal transduction by receptor tyrosine kinase[11], thereby regulating various signaling pathways, such as the epidermal growth factor receptor[12], Janus kinase (JAK)[13-15], and signal transducer and activator of transcription (STAT)[16,17] pathways. Phosphorylation is a universal and reversible posttranslational modification that plays a key role in cell growth, metabolism, and signal transduction and alters the activity of downstream targets in cell signaling pathways[18]. PTPN2 expression is high in the tissues of various tumors, including gastric adenocarcinoma, colorectal cancer[19], triple-negative breast cancer[20], non-small cell lung cancer[21], and prostate cancer[22]. PTPN family members are involved in the invasion and metastasis of many tumors, especially gastrointestinal tumors[23,24]. Kuang et al[25] showed that PTPN2 could regulate the cell cycle and apoptosis and thus affect the prognosis of pancreatic cancer patients through phosphorylation. In human colorectal cancer tissues, high expression of PTPN2 reduced the antitumor capacity in vivo by mediating tumor immune evasion[26], and PTPN2 deficiency in tumor cells was also found to increase interferon (IFN)-γ signaling and antigen presentation by T cells, as well as inhibit cytokine-induced growth, suggesting its therapeutic potential in enhancing the efficacy of immunotherapy[27]. This evidence indicates that PTPN2 is a promising prognostic and therapeutic target for tumors. However, the function of PTPN family members in HCC is incompletely elucidated.

In this study, we analyzed the value of PTPN2 in predicting the prognosis of HCC patients by immunohistochemical staining and immunoblotting experiments combined with The Cancer Genome Atlas (TCGA) database analysis to provide insight into the correlation between the expression phenotype of PTPN2 and clinical features as well as prognosis, to clarify whether PTPN2 can be used as a biological marker for HCC prognosis and to explore the effect of PTPN2 on immune evasion in HCC patients.

MATERIALS AND METHODS

Acquisition of PTPN2 expression profiles

We obtained PTPN2 expression matrix files (tumor tissue, n = 370; normal tissue, n = 50) from the TCGA database (https://www.cancer.gov/) and analyzed them via the Sento Academic website (https://www.xiantao.love/). Differential expression of PTPN2 across cancers was analyzed using the R package “limma” (P < 0.05; t test) for paired and unpaired significance analysis. Table 1 shows the clinical information of high and low expression groups of PTPN2 in HCC patients in the TCGA database.

Table 1 Clinical information of patients in high and low protein tyrosine phosphatase non-receptor 2 expression groups from The Cancer Genome Atlas database.

Characteristic	Low expression (n = 185)	High expression (n = 185)
T stage, n (%)
T1	96 (51.9)	85 (45.9)
T2	46 (24.9)	47 (25.4)
T3	35 (18.9)	45 (24.4)
T4	6 (3.2)	8 (4.3)
Unknown	2 (1.1)	0 (0)
N stage, n (%)
N0	126 (68.1)	126 (68.1)
N1	21 (11.4)	3 (1.6)
N2-3	38 (20.5)	56 (30.3)
M stage, n (%)
M0	135 (73)	131 (70.8)
M1	22 (11.9)	22 (11.9)
MX	28 (15.1)	32 (17.3)
Clinical stage, n (%)
I	91 (49.2)	80 (43.2)
II	43 (23.2)	42 (22.7)
III	38 (20.6)	47 (25.4)
IV	3 (1.6)	2 (1.1)
Unknown	10 (5.4)	14 (7.6)
Age, n (%)
≤ 65 years	105 (56.8)	127 (68.6)
> 65 years	80 (43.2)	58 (31.4)
Unknown	0 (0)	0 (0)
Sex, n (%)
Female	46 (24.9)	75 (40.5)
Male	139 (75.1)	110 (59.5)
Unknown	0 (0)	0 (0)
Grade, n (%)
G1	33 (17.8)	22 (11.9)
G2	92 (49.7)	85 (46.0)
G3	54 (29.2)	67 (36.2)
G4	4 (2.2)	8 (4.3)
Unknown	2 (1.1)	3 (1.6)

Collection of tumor samples

Samples were collected from patients who underwent liver resection at the First Affiliated Hospital of Bengbu Medical University between January 2019 and December 2021. A total of eight HCC tissue samples and matched adjacent tissues were collected for Western blot analysis, and 126 tumor tissues and corresponding wax blocks of paracancerous tissues were subjected to immunohistochemical staining. No patient had received radiation therapy, chemotherapy, or immunotherapy before surgery, and all were pathologically diagnosed with HCC after surgery. Postoperative pathological tissue samples were stored in liquid nitrogen at -80 °C to facilitate subsequent protein extraction from the tissues.

Experimental materials

Horseradish peroxidase (HRP)-conjugated anti-rabbit antibodies were purchased from Jackson ImmunoResearch Inc. The anti-β-actin primary antibody and rabbit monoclonal anti-CD3⁺/CD4⁺/CD8⁺ T-cell α antibody were purchased from Cell Signaling Technology, Inc. Tween 20 and skim milk were purchased from Sangon Biotechnology Ltd. (Shanghai, China). The rabbit anti-human PTPN2 antibody (100 μg) was purchased from Proteintech. Bovine serum albumin was purchased from Sigma-Aldrich (St. Louis, Missouri, United States).

Immunohistochemical staining

All samples were fixed with 4% paraformaldehyde, embedded into paraffin, and sliced into 4 μm slices. Following dewaxing with xylene and rehydration using ethanol gradient, antigen retrieval was carried out in citrate buffer (pH = 7.8, 0.1 mol/L) at approximately 82 °C for 24 min. The slices were then evenly covered with endogenous peroxidase blocking solution at room temperature for 15 min to block endogenous peroxidase activity. After incubation with the primary antibody of interest at 4 °C overnight, the slides were gently washed with phosphate buffer saline, incubated with biotin-conjugated secondary antibody at room temperature for 10 min, and then incubated with streptavidin-HRP anti-biotin antibody for 5 min. Finally, all sections were stained with hematoxylin and washed.

Western blot analysis

Total protein was extracted from fresh HCC tissues and corresponding adjacent tissues. Protein electrophoresis was performed using a PowerPac high voltage power supply (Bio-Rad Laboratories, Inc, CA, United States). After sodium dodecyl sulfate-polyacrylamide gel electrophoresis, total protein was electrophoretically transferred to polyvinylidene fluoride membranes. The membranes were blocked with fresh 5% skimmed milk, and then incubated with the primary antibody (anti-PTPN2 antibody, 1:800 dilution; anti-β-actin antibody, 1:3000 dilution) overnight at 4 °C. After washing with TBST, the membranes were incubated with HRP-conjugated anti-rabbit immunoglobulin G (1:3000 dilution) at room temperature for 2 h. Finally, the Bio-Rad chemical XRS imaging system (Bio-Rad laboratory) was used to visualize specific protein bands. ImageJ (version: 1.8.0) software was then used to quantify the difference of PTPN2 expression between liver cancer and normal liver tissues, which were visualized as bar graphs using GraphPad Prism (version: 9) software.

Gene Set Enrichment Analysis

In order to further explore the correlation between PTPN2 expression and HCC, we used Gene Set Enrichment Analysis (GSEA) to carry out pathway enrichment analysis on genes, sort genes according to the degree of differential expression in the two types of samples, and then test whether a predefined group of genes are at the top or bottom of the sorting table. Genes in pathways with a |normalized enrichment score| of > 1, nominal P value of < 0.05, and false discovery rate Q-value of < 0.25 were considered differentially expressed.

Prognostic analysis of PTPN2 in HCC

The TCGA database samples were divided into high and low expression groups based on the average value of PTPN2 expression The relationship between PTPN2 expression and clinicopathological characteristics (sex, age, grade, T stage, N stage, M stage, and stage). Then, clinical information of 126 HCC patients in our hospital was collected, and immunohistochemical staining was applied to detect PTPN2 in cancer tissues and the corresponding paracancerous tissues. Survival curves were then plotted after identification of independent factors for HCC prognosis by univariate and multivariate Cox analyses. Finally, we constructed a line graph-based survival prediction system using the R package “rms” by combining the PTPN2 expression level with the clinicopathological characteristics for each HCC patient to predict survival at 1, 3, and 5 years, and used calibration curves to assess the accuracy of the line graph-based prediction system for predicting survival at 1, 3, and 5 years.

Analysis of role of PTPN2 in tumor immune evasion in HCC

The downloaded hallmark shared pathway gene set was analyzed by gene set variation using the R package “GSVA” to evaluate the enrichment of hallmark pathways in the high and low PTPN2 expression groups (P < 0.05). ESTIMATE algorithm was used to estimate the stromal and immune cell populations in malignant tumor tissues and the immune cell scores of high and low PTPN2 expression groups in HCC patients were calculated.

Construction of a PTPN2-related immune profile

HCC-related marker genes in the high and low PTPN2 expression groups were analyzed with the R package “LIMA” (P < 0.05), and the correlations of 58 immune cell surface marker genes in HCC with PTPN2 expression were analyzed with the R package “reshape2” (P < 0.05). Multivariate Cox proportional hazards regression analysis was performed using prognostic related genes to further analyze the prognostic significance of PTPN2 related marker genes in HCC, so as to obtain the best candidate genes and construct an immune-related risk model. The receiver operation characteristic (ROC) curve was drawn by using the “survival ROC” package, and the area under the curve (AUC) value was obtained to evaluate the reliability of the prognostic model. In order to further analyze the relationship between the model score and clinicopathological factors (age, grade, gender, T stage, N stage, M stage, and stage), univariate and multivariate Cox regression analyses were conducted to evaluate whether the model score could be used independently of prognostic factors.

Statistical analysis

Statistical analyses were performed using R software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Log-rank test was used to compare survival data between two groups, and Kruskal-Wallis test was used to compare data among three groups or more. Student’s-t test was used to estimate the significance of normally distributed variables of quantitative data, and Wilcoxon rank sum test was used to analyze non-normally distributed variables. A P value < 0.05 was considered statistically significant.

RESULTS

Analysis of PTPN2 expression in HCC

The analysis of expression of PTPN2 in pan-cancer showed that there were significant differences in the expression of PTPN2 in many cancers, with high expression in stem cell carcinoma, bladder uroepithelial carcinoma, and bile duct carcinoma (P < 0.05, Figure 1A). Analysis of PTPN2 expression in the database with clinicopathological features of the patients revealed a correlation between the expression of PTPN2 and the sex of the patient (P < 0.05, Figure 1B). As shown by unpaired significance analysis, PTPN2 expression was higher in tumor tissues than in paracancerous tissues (P < 0.05, Figure 1C), with consistent results obtained by paired significance analysis (P < 0.05, Figure 1D). Immunohistochemical staining was applied to evaluate the expression of PTPN2 protein in the tissues, and the results showed that PTPN2 expression was significantly higher in HCC tissues than in paraneoplastic tissues (Figure 1E). To further assess the expression of PTPN2 in HCC, we measured PTPN2 mRNA expression in eight fresh frozen HCC tissue specimens and paired paracancerous tissues using quantitative real-time polymerase chain reaction (qRT-PCR) and found that the expression of PTPN2 was significantly higher in HCC tissues than in paracancerous tissues (P < 0.05, Figure 1F). Western blot analysis was performed to assess PTPN2 expression in HCC tissues and showed consistent results, with all eight tumor tissues exhibiting higher levels of PTPN2 protein expression than the paired normal tissues (P < 0.05, Figure 1G and H).

Figure 1 Expression levels of protein tyrosine phosphatase non-receptor 2 in tumors. A: Differences in protein tyrosine phosphatase non-receptor 2 (PTPN2) expression in tumor and normal tissues in pan-tumor species in The Cancer Genome Atlas (TCGA) database; B: Correlation of PTPN2 expression with patient sex; C and D: Unpaired and paired significance analysis of PTPN2 expression in the TCGA database; E: Immunohistochemical analysis of 126 pairs of paraffin-embedded HCC tissues and adjacent normal tissues showing higher PTPN2 expression in the HCC tissues; F: Results of quantitative real-time polymerase chain reaction analysis of PTPN2 mRNA expression in eight pairs of HCC tissues and corresponding normal tissues; G: Western blot analysis comparing the protein expression of PTPN2 in eight pairs of HCC tissues and normal tissues and quantitative analysis of significance; H: Quantitative analysis of the results of Western blot to compare tumor tissues and normal tissues; I: Kaplan-Meier survival analysis based on PTPN2 expression in 126 hepatocellular carcinoma (HCC) patients. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001. PTPN2: Protein tyrosine phosphatase non-receptor 2; OS: Overall survival.

To further investigate the role played by PTPN2 in HCC, we collected tissue specimens from 126 patients with HCC in our hospital and evaluated the expression of PTPN2 by immunohistochemical staining. Among the 126 paired HCC patient samples, PTPN2 was highly expressed in the tumor tissues in 85 patients and in the paraneoplastic tissues in 41. Comparison of PTPN2 expression in HCC tumor tissues and paraneoplastic tissues showed that the expression of PTPN2 was higher in HCC tumor tissues than in paraneoplastic tissues (P < 0.001) (Table 2). Survival analysis showed that by the end of follow-up, the shortest overall survival (OS) time was 0.3 mo, and the longest OS time was 49 mo among the 126 HCC patients. The expression of PTPN2 was negatively correlated with the prognosis of patients, suggesting that the group with high PTPN2 expression had a poor prognosis (P < 0.05, Figure 1I).

Table 2 Protein tyrosine phosphatase non-receptor 2 expression in hepatocellular carcinoma tumor tissues and paracancerous tissues.

Tumor tissue	Paracancer tissue		Total	χ2	P value
	High expression	Low expression
High expression	16	69	85	12.163	< 0.001
Low expression	20	21	41
Total	36	90

Correlation analysis of PTPN2 expression with clinicopathological features showed that the expression of PTPN2 was correlated with the preoperative α-fetoprotein (AFP) level (P < 0.027) but was not significantly correlated with sex (P = 0.221), age (P = 0.311), coinfection status (P > 0.651), cirrhosis status (P = 0.956), tumor diameter (P = 0.482), or degree of differentiation (P = 0.516) (Table 3).

Table 3 Clinical information of high and low protein tyrosine phosphatase non-receptor 2 expression groups of patients from the Bengbu Medical College database.

Characteristic	Total (n)	PTPN2 expression		χ2	P value
		Low (n = 41)	High (n = 85)
Sex, n (%)
Female	23	5 (3.9)	18 (14.3)	1.495	0.221
Male	103	36 (28.6)	67 (53.2)
Age
≤ 65 years	91	32 (25.4)	59 (46.8)	1.028	0.311
> 65 years	35	9 (7.2)	26 (20.6)
Viral hepatitis, n (%)
Yes	110	35 (27.8)	75 (59.5)	0.205	0.651
No	16	6 (4.8)	10 (7.9)
Liver cirrhosis, n (%)
Yes	61	20 (15.9)	41 (32.5)	0.003	0.956
No	65	21 (16.7)	44 (34.9)
AFP (ng/mL), n (%)
≤ 25	52	24 (19.0)	32 (25.4)	4.888	0.027
> 25	74	17 (14.5)	53 (42.1)
Tumor size, n (%)
≤ 5 cm	65	23 (18.3)	42 (33.3)	0.495	0.482
> 5 cm	61	18 (14.3)	43 (34.1)
Degree of differentiation, n (%)
Undifferentiated	29	7 (5.6)	22 (17.5)	1.324	0.516
Moderately differentiated	76	26 (20.6)	50 (39.7)
Highly differentiated	21	8 (6.3)	13 (10.3)

PTPN2: Protein tyrosine phosphatase non-receptor 2; AFP: α-fetoprotein.

Univariate Cox regression analysis of the patient clinicopathological data showed that the factors associated with OS were PTPN2 expression [P = 0.018, 95% confidence interval (CI): 1.123-3.359, hazard ratio (HR) = 1.942] and the concomitant presence of cirrhosis (P = 0.018, 95%CI: 1.109-2.930, HR = 1.802), while the factors not associated with PTPN2 expression were sex (P = 0.940), age (P = 0.334), presence of coinfection with virus (P = 0.838), AFP level (P = 0.258), tumor diameter (P = 0.649), and degree of differentiation (P = 0.967) (Table 4). Multivariate Cox regression analysis showed that PTPN2 expression (P = 0.018, 95%CI: 1.122-3.386, HR = 1.949) and the concomitant presence of cirrhosis (P = 0.021, 95%CI: 1.091-2.958, HR = 1.796) were independent risk factors affecting the prognosis of HCC patients (P < 0.05) (Table 5).

Table 4 Univariate analysis of clinicopathological factors and overall survival in 126 hepatocellular carcinoma patients.

Characteristic	P value	HR	95%CI
			Lower	Upper
Sex	0.940	0.977	0.532	1.792
Age	0.334	0.762	0.439	1.322
Hepatitis virus coinfection	0.838	1.585	0.441	1.942
Liver cirrhosis	0.018	1.802	1.109	2.930
AFP level	0.258	1.326	0.813	2.163
Tumor size	0.649	1.119	0.689	1.818
Degree of differentiation	0.967	0.992	0.683	1.441
PTPN2	0.018	1.942	1.123	3.359

PTPN2: Protein tyrosine phosphatase non-receptor 2; AFP: α-fetoprotein; HR: Hazard ratio; CI: Confidence interval.

Table 5 Cox multivariate analysis of prognostic factors in hepatocellular carcinoma patients.

Characteristic	B	SE	Wald	P value	HR	95%CI
						Lower	Upper
Age	-0.272	0.283	0.926	0.336	0.762	0.438	1.326
AFP level	0.082	0.258	0.102	0.749	1.086	0.655	1.799
Liver cirrhosis	0.586	0.254	5.303	0.021	1.796	1.091	2.958
Degree of differentiation	0.138	0.250	0.303	0.582	1.148	0.703	1.875
PTPN2 expression	0.667	0.282	5.607	0.018	1.949	1.122	3.386

PTPN2: Protein tyrosine phosphatase non-receptor 2; AFP: α-fetoprotein; HR: Hazard ratio; CI: Confidence interval.

GSEA for oncogenic pathway enrichment

GSEA indicated that PTPN2 is involved in multiple signaling pathways in HCC. The top 20 upregulated signaling pathways associated with PTPN2 are shown in Table 6 based on GSEA scores in HCC (Table 6). We found that PTPN2 was associated with the JAK-STAT signaling pathway, MAPK signaling pathway, cancer-related pathways, and Notch signaling pathway (Figure 2).

Figure 2 Gene Set Enrichment Analysis of oncogenic pathways upregulated by protein tyrosine phosphatase non-receptor 2 in hepatocellular carcinoma.

Table 6 Protein tyrosine phosphatase non-receptor 2 oncogenic pathway parameters in Gene Set Enrichment Analysis.

GeneSet	NES	NOM P value	FDR Q value
KEGG_OOCYTE_MEIOSIS	2.15	0.000	0.000
PROGESTERONE_MEDIATED_OOCYTE_MATURATION	2.09	0.000	0.004
PHOSPHATIDYLINOSITOL_SIGNALING_SYSTEM	2.03	0.000	0.006
INOSITOL_PHOSPHATE_METABOLISM	2.03	0.000	0.005
NEUROTROPHIN_SIGNALING_PATHWAY	2.02	0.000	0.005
PANCREATIC_CANCER	2.02	0.000	0.004
PATHWAYS_IN_CANCER	2.01	0.000	0.003
REGULATION_OF_AUTOPHAGY	2.01	0.000	0.003
ENDOCYTOSIS	2.00	0.000	0.004
UBIQUITIN_MEDIATED_PROTEOLYSIS	1.99	0.000	0.004
NOTCH_SIGNALING_PATHWAY	1.99	0.000	0.003
REGULATION_OF_ACTIN_CYTOSKELETON	1.98	0.000	0.003
CELL_CYCLE	1.97	0.000	0.004
APOPTOSIS	1.97	0.000	0.003
FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS	1.97	0.000	0.003
JAK_STAT_SIGNALING_PATHWAY	1.97	0.002	0.003
MAPK_SIGNALING_PATHWAY	1.96	0.000	0.003
SPLICEOSOME	1.96	0.000	0.003
SMALL_CELL_LUNG_CANCER	1.96	0.000	0.003
INSULIN_SIGNALING_PATHWAY	1.95	0.000	0.003

NES: Normalized Enrichment Score; NOM: Nominal; FDR: False discovery rate.

Effect of PTPN2 expression on the tumor immune microenvironment in HCC

In order to explore the effect of PTPN2 expression on the tumor immune microenvironment (TIME) in HCC, we used the R package “GSVA” to analyze the enrichment of hallmark pathway gene sets in the groups with high and low PTPN2 expression. In the PTPN high expression group, the Wnt signaling pathway, Hedgehog signaling pathway, interleukin (IL)6-JAK-STAT3 signaling pathway, IL2-STAT5 signaling pathway, KRAS signaling pathway, and inflammatory response were upregulated (Figure 3A). The heat map was constructed using single-sample GSEA and the difference analysis of immune cells and function in HCC showed that the immune infiltration in the group with high PTPN2 expression was higher than that in the group with low PTPN2 expression (Figure 3B). Analysis of stromal and immune cells using ESTIMATE algorithm in HCC also showed that the immune cell score of the group with high PTPN2 expression was significantly higher than that of the group with low PTPN2 expression (Figure 3C), indicating that high PTPN2 expression is associated with immune cell infiltration. Further analysis showed that epithelial mesenchymal transition, angiogenesis, transforming growth factor (TGF)-β signaling pathway, Notch signaling pathway, and Hedgehog signaling pathway were significantly up-regulated in the group with high PTPN2 expression compared with the group with low PTPN2 expression. Thus, high expression of PTPN2 mediates immune escape in HCC (Figure 3D). The expression level of programmed death ligand 1 (PD-L1) is an important indicator for predicting anti-PD-1/L1 response, and an important therapeutic target in tumor immunotherapy. We further studied the expression of PD-L1 to evaluate the effect of immunotherapy, and found that the expression of PD-L1 in the group with high expression of PTPN2 was higher than that in the group with low expression of PTPN2, suggesting that the group with high expression of PTPN2 may have immune evasion ability and better effect of immunotherapy (Figure 3E).

Figure 3 Analysis of relationship between protein tyrosine phosphatase non-receptor 2 expression and the immune microenvironment in hepatocellular carcinoma. A: Enrichment of differentially expressed genes between the high and low protein tyrosine phosphatase non-receptor 2 (PTPN2) expression groups in the hallmark pathways of hepatocellular carcinoma (HCC). The results showed that genes upregulated in PTPN2-high vs PTPN2-low samples in HCC mainly regulate immune activation and inflammatory response through the Wnt signaling pathway, Hedgehog signaling pathway, interleukin (IL)6-Janus kinase-signal transducer and activator of transcription (STAT)3 signaling pathway, IL2-STAT5 signaling pathway, KRAS signaling pathway, and inflammatory response; B: Differential functional analysis of immune cells in the high and low PTPN2 expression groups of patients with HCC. Blue represents high expression samples, and red represents low expression samples; C: Differential analysis of interstitial signaling pathways in the high and low PTPN2 expression groups of patients with HCC and assessment of immune cell scores. Blue represents high expression samples, and red represents low expression samples; D and E: Immunotherapeutic effect in the high and low PTPN2 expression groups calculated with the ESTIMATE algorithm; ^aP < 0.05; ^bP < 0.01; ^cP < 0.001; ns: P > 0.05. PTPN2: Protein tyrosine phosphatase non-receptor 2.

Immunoprognostic analysis of PTPN2 in HCC

We identified 58 immune genes that were associated with PTPN2 expression and localized on the surface of immune cells. The proteins encoded by these genes are also known as immunomodulators and are classified as immune stimulants or immunosuppressants. Differential expression analysis showed significant differences in the expression of 48 of the 58 PTPN2-related immunomodulators between the high and low PTPN2 expression groups (Table 7). In the Cox survival analysis, the PTPN2 expression-related genes CD276, TNFRSF4, TNFSF4, TGFB1, and TGFBR1 were identified as prognosis-related risk factors (Figure 4A). We constructed a PTPN2-related immune risk model using prognostic immunomodulators (Figure 4B). The formula for calculating the risk score was as follows.

Figure 4 Risk model construction based on protein tyrosine phosphatase non-receptor 2-associated immunomodulators in hepatocellular carcinoma. A: Prognostic immunomodulators associated with protein tyrosine phosphatase non-receptor 2 (PTPN2) in hepatocellular carcinoma (HCC); B and C: Cox proportional hazard regression model for prediction of HCC risk constructed with PTPN2-associated immune checkpoints TNFRSF4 and TNFSF4; D: According to the risk model, the prognosis of the high-risk group was significantly worse than that of the low-risk group; E and F: Univariate and multivariate Cox regression analyses of model risk score and clinical factors; G: Accuracy of the Cox regression model for risk prediction assessed using receiver operation characteristic curves. AUC: Area under the curve. ^aP < 0.05; ^bP < 0.01.

Table 7 Immune checkpoints associated with protein tyrosine phosphatase non-receptor 2 in hepatocellular carcinoma.

Gene	Correlation	P value
ADORA2A	0.151	0.0036a
BTLA	0.211	0a
CD160	0.164	0.0015a
CD244	0.099	0.0574
CD274	-0.012	0.817
CD96	0.305	0a
CSF1R	0.214	0a
CTLA4	0.307	0a
HAVCR2	0.315	0a
IDO1	0.075	0.15
IL10RB	0.229	0a
KDR	-0.187	0.0003a
LAG3	0.185	0.0003a
LGALS9	0.309	0a
PDCD1	0.344	0a
PDCD1LG2	0.139	0.0074a
PVRL2	0.217	0a
TGFB1	0.34	0a
TGFBR1	0.121	0.019a
TIGIT	0.268	0a
VTCN1	0.11	0.0333a
C10orf54	0.084	0.104
CD27	0.284	0a
CD276	0.326	0a
CD28	0.119	0.022a
CD40	-0.11	0.0336a
CD40LG	0.291	0a
CD48	0.335	0a
CD80	0.215	0a

^aP < 0.05.

The immune risk model was used to classify TCGA samples according to risk and prognosis, and the risk prediction ability of the Cox model was assessed using ROC curves and found to have good accuracy (Figure 4C). According to the model, the survival probability was significantly lower in the high-risk group than in the low-risk group (Figure 4D). In addition, univariate Cox regression analysis showed that the tumor-node-metastasis (TNM) stage and risk score were prognostically relevant risk factors (Figure 4E), and multivariate Cox regression analysis showed that the TNM stage and risk score could be independent prognostic risk factors (Figure 4F). These analyses suggest that the PTPN2-mediated TIME in HCC is associated with a poor prognosis. Accuracy assessment of the risk model ROC curve indicated that the model risk score (AUC = 0.607) and the model risk score combined with clinical factors (AUC = 0.721) had high accuracy (Figure 4G).

Immune cell infiltration in high and low PTPN2 expression groups

To further verify that PTPN2 mediates immune evasion in HCC, we selected HCC tissue samples graded as having “+” and “++” PTPN2 expression (see Figure 1E) for immunohistochemical analysis. The infiltrations of IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells in HCC tumor tissues and surrounding tissues were detected to further clarify whether PTPN2 mediates tumor immune escape. In “++” HCC tissues, IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells mainly congregated in the peritumoral stroma, and few immune cells penetrated the stroma into the tumor parenchyma (Figure 5). In tissues with “+” PTPN2 expression, peripheral blood IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells clustered less around the tumor, and immune cells infiltrated more into the tumor parenchyma (Figure 5). It was further confirmed that the high expression of PTPN2 mediates immune escape of HCC tumor cells.

Figure 5 Immune cell infiltration in high and low protein tyrosine phosphatase non-receptor 2 expression groups. ++: Interleukin (IL)-6⁺ T-cell, CD3⁺ T-cell, CD4⁺ T-cell and CD8⁺ T-cell infiltration in the high protein tyrosine phosphatase non-receptor 2 (PTPN2) expression group; +: IL-6⁺ T-cell, CD3⁺ T-cell, CD4⁺ T-cell, and CD8⁺ T-cell infiltration in the low PTPN2 expression group. IL: Interleukin; PTPN2: Protein tyrosine phosphatase non-receptor 2.

DISCUSSION

The PTPN2 gene is located on chromosome 18p11.3-p11.2, and the encoded protein is a member of the PTP family[28]. PTPN2 has been shown to dephosphorylate tyrosine kinases and is a commonly expressed nonreceptor phosphatase whose role in tumors has been slowly discovered in recent years. PTPN2 was historically believed to play a tumor-suppressive role in human cancers, but as an increasing number of comprehensive and detailed studies have been conducted, PTPN2 has been found to have significant procancer effects, and patients with high PTPN2 expression often have a poor prognosis. Young et al[29] found that PTPN2 performs a protumor function in B-cell lymphoma and that PTPN2 promotes the proliferation of murine B-cell lymphoma cells, with the proliferation of tumor cells significantly diminished when PTPN2 is depleted. We investigated the role played by PTPN2 in HCC and demonstrated for the first time that PTPN2 is overexpressed in HCC through TCGA database analysis and immunohistochemical staining, immunoblotting experiments, and qRT-PCR. Moreover, survival analysis revealed that the expression of PTPN2 is correlated with a poor prognosis of patients and that patients with high PTPN2 expression had a poorer prognosis. Furthermore, univariate and multivariate Cox proportional hazards regression analysis showed that PTPN2 was an independent prognostic risk factor for HCC and could be used as a biomarker for poor prognosis of HCC.

There is growing evidence that members of the PTPN family can regulate the development and differentiation of immune cells, and thus regulate autoimmunity, while also providing opportunities for tumors to evade surveillance by the immune system. Therefore, targeting PTPNs can activate the body’s immune system and thus enhance the efficacy of anti-tumour immunity. It has been shown that PTPN2 deficiency promotes T-cell-mediated immune surveillance and antitumor activity in different tumor contexts[30]. PTPN2 deficiency promotes the differentiation of naive T cells, T follicular helper cells, and B cells, exacerbating autoimmune formation[31]. The p53 gene is widely linked to cancer susceptibility, and p53 heterozygosity was found to lead to lung and liver adenocarcinoma in 44% of mice at 17 mo of age[32]. Wiede et al[30] by silencing the PTPN2 gene in T cells, found that tumorigenesis was significantly reduced in mice with PTPN2 deficiency and that PTPN2 deficiency in T cells prevented p53-induced tumor formation; in addition, CD4⁺ and CD8⁺ effector/memory T cells were significantly more active in PTPN2-deficient mice, consistent with our results of immunohistochemical detection of CD4⁺ and CD8⁺ in cells. Moreover, we found that in HCC tissues with “++” PTPN2 expression, IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells were clustered mainly in the peritumoral stroma, with few immune cells penetrating the stroma into the tumor parenchyma, thus mediating tumor immune escape. In contrast, in PTPN2 “+” tissues, less aggregation of tumor peripheral IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells was observed, and more immune cells penetrated the stroma into the tumor parenchyma. Manguso et al[27] performed a loss-of-function screen by CRISPR gene editing in tumor cells, and PTPN2 was identified as the preferred choice for T-cell recruitment and for increasing tumor sensitivity to anti-PD-1 immunotherapy. Moreover, upon forced overexpression of PTPN2 in tumor cells, this sensitivity to immunotherapy was eliminated in vivo, rendering tumor cells resistant to the effects of immunotherapy and mediating tumor immune escape. Furthermore, after silencing the expression of PTPN2, it was determined that this effect was achieved by enhancing IFN-α signaling[33]. In addition, deletion of PTPN2 enhanced the antitumor response and checkpoint blockade effect of CD8⁺ T cells, thus improving tumor immunity. Furthermore, the absence of PTPN2 enhances the efficacy of anti-PD1 therapy and CAR-T in solid tumors[30]. Currently, many PTPNs have been reported to be relevant for tumour immunotherapy and have spawned a variety of inhibitors that, in combination with anti-PD-1 and anti-PD-L1 therapies, can significantly improve the malignant characteristics of tumors[34-36]. Therefore, the further development of novel inhibitors of PTPNs and the study of their safety are urgently needed for the future conquest of cancer. In conclusion, our results showed that PTPN2 was highly expressed in HCC cells and mediated tumor immune escape by regulating the aggregation of IL-6⁺, CD3⁺, CD4⁺, and CD8⁺ T cells, resulting in a significantly worse prognosis for patients in the high PTPN2 expression group than in the low PTPN2 expression group.

In this study, we analyzed the expression of surface marker proteins of immune cells related to PTPN2, and constructed a Cox proportional hazard regression model using prognostic marker genes. The risk score of the model was identified as an independent prognostic factor. The immune score also showed that the immune escape ability in the high-risk group was significantly higher than that in the low-risk group, which affected the prognosis of HCC patients.

CONCLUSION

This study provides a comprehensive analysis of PTPN2 and elucidates the key potential role of PTPN2 as a therapeutic target and diagnostic biomarker for improving survival in HCC patients.

ACKNOWLEDGEMENTS

We would like to express our sincere appreciation for the curators of the platforms and datasets from the open databases The Cancer Genome Atlas, Sento Academic, EXPRESS, SurvivalMeth, MSigDB, and TIDE.